Directional drilling intelligent drilling system and control method based on digital twinning and multi-source information fusion

Abstract

The application belongs to the technical field of deep exploration, and discloses a directional drilling intelligent drilling system and control method based on digital twinning and multi-source information fusion, which solves the problems of multi-source data heterogeneous island, parameter inversion multi-solution lag, passive risk evaluation and non-virtual and real closed-loop intelligent regulation in the existing directional drilling technology. The underground sensing and executing unit collects multi-source heterogeneous data and uploads them to the ground intelligent decision platform. The platform pre-processes the data, updates the digital twinning model based on the extended Kalman filter, and inverses the rock mass mechanical parameters and the geological state in front of the drill bit. The twinning model is used for multi-working-condition advanced deduction, so as to obtain the optimal drilling control strategy and issue it to the underground executing mechanism. The application can improve the safety, trajectory accuracy and drilling efficiency of directional drilling construction, and is suitable for deep exploration.

Description

Technical Field

[0001] This invention belongs to the field of deep earth exploration technology, specifically relating to a directional drilling intelligent drilling system and control method based on digital twin and multi-source information fusion. Background Technology

[0002] During the construction of deep-buried tunnels, the extreme geological environment, including high ground stress, high rock temperature, and complex seepage fields, necessitates accurate perception of the surrounding rock condition and proactive early warning of construction risks to ensure construction safety and optimize support design. Existing tunnel surrounding rock stability monitoring and drilling control technologies have several shortcomings:

[0003] (1) The silo effect of multi-source heterogeneous data is serious, and the real-time collaborative sensing capability is insufficient:

[0004] Current monitoring systems typically consist of multiple sensing systems, including seismic detectors, electrical electrodes, distributed optical fibers, and pressure cells. Each system is deployed and operates independently, lacking a unified spatiotemporal reference calibration. The sampling frequencies of different sensors vary significantly, and there are axial and radial physical installation gaps, making it difficult to accurately register the acquired physical field data spatially and to strictly synchronize them temporally. This fragmented data sensing mode makes it impossible to quantify the mutual interference effects between multi-source data, hindering the formation of a global and coordinated description of the surrounding rock's physical state.

[0005] (2) The parametric inversion suffers from significant ambiguity and lag, making it difficult to characterize dynamic evolution processes:

[0006] Existing technologies largely rely on single physical field test results to infer geological parameters, which is limited by the inherent redundancy and multiple solutions in physical property interpretation. For example, observed wave velocity anomalies may be caused by deterioration of rock mass integrity, or they may originate from changes in pore pressure or water-bearing state. It is difficult to make accurate geological judgments based on data from a single dimension. In addition, inversion algorithms are mostly discrete and static, and the time required from data acquisition and transmission to model calculation is long. They cannot sensitively capture the transient dynamic process of microfracture initiation, expansion, and connection in the surrounding rock under tunneling disturbance, resulting in a significant lag between geological perception results and actual working conditions.

[0007] (3) Stability assessment relies on human experience and static grading, lacking forward-looking simulation capabilities:

[0008] Most existing rock mass stability assessment systems (such as RMR, Q systems, and their derivatives) still belong to the typical "passive response" model, focusing on the graded evaluation or "post-event analysis" of existing monitoring data. This model lacks predictive extrapolation of the evolution trend of the surrounding rock and cannot predict the path of risk evolution based on dynamic factors such as construction tunneling pressure and cyclic unloading. Especially when facing sudden disasters such as water inrush and rock bursts, existing systems are unable to simulate the stress response and seepage channel evolution of the surrounding rock under different construction actions in advance in the digital space, resulting in a serious lack of lead time for identification.

[0009] (4) The system lacks cognitive self-learning ability and lacks a virtual-real closed-loop control architecture:

[0010] Most automated monitoring systems are still in the "sensory monitoring" stage, and have not yet established a deeply coupled digital twin model between the surrounding rock geological environment, physical fracturing mechanism, and construction support parameters. These systems can only achieve simple threshold alarms or linear predictions, failing to achieve a closed-loop upgrade from "sensed signals" to "logical deduction" and then to "active control." Due to the lack of a model-in-the-loop intelligent decision engine, the system struggles to automatically optimize tunneling speed or support timing based on the inverted real-time mechanical parameters, hindering the advancement of tunnel engineering towards intelligent and unmanned construction.

[0011] In summary, how to achieve spatiotemporal assimilation of multi-source information of surrounding rock, accurate inversion of physical property parameters, and forward-looking risk control based on digital twins under complex geological conditions has become a key technical challenge that urgently needs to be solved in the field of tunnel engineering. Summary of the Invention

[0012] The technical problem to be solved by this invention is to provide an intelligent directional drilling system and control method based on digital twin and multi-source information fusion, which solves the problems of heterogeneous islands of multi-source data, multiple lags in parameter inversion, passive risk assessment, and lack of virtual and real closed-loop intelligent control in existing directional drilling technologies.

[0013] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0014] On the one hand, the present invention provides a directional drilling intelligent drilling system based on digital twin and multi-source information fusion, including: a downhole sensing and execution unit, a data transmission network and a ground intelligent decision-making platform;

[0015] The downhole sensing and execution unit is used to collect multi-source heterogeneous data during the drilling process in real time and execute drilling control commands.

[0016] The data transmission network is used to upload multi-source heterogeneous data collected by the downhole sensing and execution unit to the ground intelligent decision-making platform, and to send control commands generated by the ground intelligent decision-making platform to the downhole sensing and execution unit;

[0017] The ground-based intelligent decision-making platform is used to assimilate multi-source heterogeneous data, update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit, use the digital twin model to perform working condition simulation based on the inverted geological conditions in front of the drill bit, generate the optimal drilling control strategy based on the simulation results, and send the corresponding control commands to the downhole sensing and execution unit through the data transmission network.

[0018] This solution constructs an intelligent drilling system comprising a downhole sensing and execution unit, a data transmission network, and a surface intelligent decision-making platform. The system collects multi-source heterogeneous data through the downhole sensing and execution unit and uploads it to the surface intelligent decision-making platform via the data transmission network. The platform assimilates this data to update the digital twin model and invert the geological conditions within a certain range ahead of the drill bit. The inverted geological conditions are then substituted into the digital twin model for advanced working condition simulation. Based on the simulated working conditions, an optimal drilling control strategy is generated and sent to the downhole sensing and execution unit, thus forming a closed-loop control system during the drilling process. The introduction of the digital twin model enables advanced simulation and trend prediction capabilities during construction, allowing for the prediction of drilling risks based on real geological parameters. This avoids the decision-making lag and risk omissions caused by traditional systems relying solely on experience.

[0019] Furthermore, the downhole sensing and execution unit is integrated at the end of the drill string assembly and includes a multi-functional measurement while drilling module, a borehole wall panoramic imaging component, and a drilling execution mechanism;

[0020] The multi-functional measurement while drilling module is used to collect drilling mechanical parameters and geophysical logging data;

[0021] The borehole wall panoramic imaging component is used to acquire panoramic image data of the borehole wall;

[0022] The drilling actuator is used to receive and execute drilling control commands.

[0023] In this solution, the downhole sensing and execution unit is integrated into the end of the drill string assembly, which can shorten the distance between the sensor and the drill bit and reduce the depth lag and transmission delay of data acquisition. Through the multi-functional measurement while drilling module and the borehole wall panoramic imaging component, mechanical, geophysical and visual data are acquired respectively, realizing the synchronous acquisition of multi-dimensional and multi-modal data, and providing a comprehensive data source for geological state inversion.

[0024] Furthermore, the ground-based intelligent decision-making platform includes a data assimilation module, a digital twin model module, and an intelligent decision-making and control module;

[0025] The data assimilation module is used to perform spatiotemporal alignment of multi-source heterogeneous data;

[0026] The digital twin model module is used to update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit. Based on the inverted geological conditions in front of the drill bit, the digital twin model is used to perform working condition simulation.

[0027] The intelligent decision-making and control module is used to generate the optimal drilling control strategy based on the deduction results of the digital twin model, and to issue corresponding control commands to the drilling execution mechanism.

[0028] In this solution, the data assimilation module achieves spatiotemporal unification and format standardization of multi-source heterogeneous data, eliminating data silos and spatiotemporal misalignment issues; the digital twin model module undertakes the core functions of model updating, geological inversion, and advanced extrapolation, enabling real-time synchronization between the virtual space and the physical drilling process; the intelligent decision-making and control module outputs executable control strategies based on the extrapolation results, realizing a complete link from data to decision-making to execution, and improving the intelligence and foresight of drilling control.

[0029] On the other hand, based on the above system, the present invention also provides a method for intelligent directional drilling control based on digital twin and multi-source information fusion, comprising the following steps:

[0030] S1. Real-time acquisition of multi-source heterogeneous data during the drilling process;

[0031] S2. Assimilate the multi-source heterogeneous data;

[0032] S3. Update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit;

[0033] S4. Based on the geological conditions ahead of the drill bit derived by inversion, the working conditions are simulated using a digital twin model, and the optimal drilling control strategy is generated based on the simulation results.

[0034] S5. Generate corresponding control commands based on the optimal drilling control strategy and issue them to the drilling actuator.

[0035] In this scheme, a complete intelligent drilling control process is formed by sequentially executing data acquisition, assimilation processing, model update and inversion, working condition simulation, strategy generation and command issuance. Based on multi-source data and with digital twin as the core, this method realizes the transformation of the drilling process from passive monitoring to active prediction and control. It can accurately match the geological conditions and adaptively adjust the drilling behavior, thereby improving the safety, trajectory accuracy and drilling efficiency of directional drilling construction.

[0036] Furthermore, in step S1, the multi-source heterogeneous data includes: drilling condition data, geophysical response data, and borehole wall panoramic visual data; the drilling condition data includes drilling pressure, rotation speed, torque, and drilling fluid discharge; the geophysical response data includes natural gamma logging values ​​and formation resistivity.

[0037] This scheme clarifies that multi-source heterogeneous data consists of three types of data: drilling conditions, geophysical response, and borehole wall visual data. These data cover mechanical control quantities, formation property quantities, and intuitive characteristics of rock mass structure. Drilling condition data reflects the drilling operation status, geophysical response data is used to identify lithology and water content, and borehole wall visual data intuitively reflects the fracture and fracturing state. The three types of data complement and constrain each other, enabling a comprehensive and accurate characterization of drilling conditions and geological conditions.

[0038] Furthermore, in step S2, the assimilation process for the multi-source heterogeneous data includes:

[0039] S21. Unified time and space reference: using a high-precision master clock as the reference time axis. The current depth of the drill bit is used as the spatial axis. This maps multi-source heterogeneous data to the same time-depth coordinate system.

[0040] S22. Depth Hysteresis Correction: Establish a dynamic buffer queue to forward-correct and map data collected by sensors located behind the drill bit to the current actual contact depth of the drill bit. ;

[0041] S23. Heterogeneous Data Vectorization: Convert drilling condition data and geophysical response data into standardized numerical vectors respectively.

[0042] Drilling condition vector at time step , represented as ; For drilling pressure; Rotational speed; Torque; This refers to the drilling fluid discharge rate; Represents the matrix transpose operation;

[0043] depth Geophysical vector at location , represented as ; Natural gamma logging value; Formation resistivity; Represents the matrix transpose operation;

[0044] S24. Dimensionality Reduction of Visual Features in Hole Wall Images: A lightweight convolutional neural network (CNN) is used to segment and extract features from panoramic images of hole walls, outputting a fracture development density index. Texture index of hole wall roughness To form visual feature vectors , represented as , This represents the matrix transpose operation.

[0045] In this scheme, the time asynchrony caused by inconsistent sampling frequencies of different sensors is solved by unifying the spatiotemporal reference; the depth mapping deviation caused by the physical distance between the sensor and the drill bit is eliminated by depth lag correction, ensuring that the data strictly corresponds to the real geological location; different types of data are converted into a standard format that the model can recognize by vectorizing heterogeneous data; and unstructured images are converted into lightweight numerical features by visual feature dimensionality reduction. The four steps of processing together realize the spatiotemporal alignment, format unification and effective dimensionality reduction of multi-source data, providing high-quality input for subsequent model inversion.

[0046] Furthermore, in step S3, the method of updating the digital twin model and inverting the geological conditions in front of the drill bit based on the assimilated data includes:

[0047] S31. Define the digital twin state vector :

[0048] ;in, It represents the uniaxial compressive strength of the rock. The internal friction angle of the rock; This refers to drill bit wear. Represents the matrix transpose operation;

[0049] S32. Establish the state prediction equation:

[0050] Based on the dynamic mechanism of rock fracturing, and according to the state at the previous moment and the current drilling condition vector Predict the prior estimate of the state at the current moment. :

[0051] ;in, This is a nonlinear rock fracturing dynamics function; This refers to the process noise from the previous moment;

[0052] S33. Constructing multi-source fusion observation vectors and observation equations:

[0053] The assimilated mechanical data, geophysical data, and visual feature data are uniformly constructed into an observation vector. :

[0054] ;

[0055] in, for Torque at any given moment; for Mechanical drilling speed at any given moment; For depth Geophysical vector at the location; For depth Visual feature vector at the location; Represents the matrix transpose operation;

[0056] Establish a nonlinear mapping relationship between the state vector and the observation vector, and construct the observation equation:

[0057] ;

[0058] in, It is a nonlinear observation function; To observe noise;

[0059] S34. Use a filtering algorithm for state correction to achieve parameter inversion:

[0060] Calculate Kalman gain Using observation vectors Compared with predicted observations The residuals between the two conditions are used to correct the prior state estimate, resulting in the optimal posterior geological state estimate. :

[0061] ;

[0062] S35. Real-time synchronous updating of the digital twin model: This involves updating the obtained posterior geological state estimate. By incorporating the digital twin model, the geological conditions, rock mechanics parameter distribution, and drilling tool working status of the virtual borehole are updated.

[0063] In this scheme, a state vector containing rock mass strength, internal friction angle, and drill bit wear is defined to clarify that the inversion target is strongly correlated with drilling control; a prediction equation is established based on rock fracture dynamics, which conforms to the physical mechanism of drilling; an observation vector integrating torque, mechanical drilling rate, geophysical data, and visual features is constructed to achieve multi-source information joint constraint inversion; an extended Kalman filter is used to correct the state, which can suppress noise, reduce inversion ambiguity, and improve the accuracy and real-time performance of geological state estimation; the inversion results are synchronized to the digital twin model to ensure high-fidelity synchronization between the virtual model and the physical drilling process.

[0064] Furthermore, in step S4, the working condition simulation includes: simulating in parallel multiple sets of drilling parameter combinations the hole wall stability, trajectory deviation, and risk probability.

[0065] In this scheme, by simulating multiple sets of drilling parameter combinations in parallel in a digital twin space, the stability of the borehole wall, trajectory control accuracy, and probability of disaster risk corresponding to different parameters can be predicted in advance without affecting the actual construction. The simulation results are based on real-time updated geological models and rock mass parameters, which are close to the real working conditions and can provide quantitative basis for the selection of control strategies, thereby improving the scientific nature and foresight of decision-making.

[0066] Furthermore, in step S4, the optimal drilling control strategy includes:

[0067] It adaptively adjusts drilling pressure, rotation speed, and drilling fluid discharge, dynamically corrects the borehole trajectory, and actively suppresses and controls borehole instability, fracture zones, and water inrush risks.

[0068] In this scheme, drilling parameters are matched with rock drillability in real time by adaptively adjusting drilling pressure, rotation speed, and drilling fluid discharge, balancing drilling efficiency and borehole stability. By dynamically correcting the borehole trajectory, the directional drilling trajectory is ensured to meet the design accuracy requirements. By actively suppressing the risks of borehole instability, fracture zones, and water inrush, construction risks can be avoided in advance, preventing accidents such as stuck drill, borehole collapse, and water inrush, thus achieving safe, efficient, and intelligent directional drilling.

[0069] The beneficial effects of this invention are:

[0070] (1) Achieve spatiotemporal synchronization of multi-source heterogeneous data and eliminate data silos:

[0071] This invention achieves spatiotemporal alignment of heterogeneous data such as drilling pressure, rotational speed, torque, drilling fluid discharge, natural gamma, resistivity, and panoramic borehole images by unifying the time-depth benchmark, depth lag correction, heterogeneous data vectorization, and visual feature dimensionality reduction. This solves the problems of inconsistent sampling frequencies, sensor position offsets, data asynchrony, and spatial misregistration in traditional monitoring systems, providing a high-fidelity and highly consistent data foundation for geological inversion and digital twins.

[0072] (2) Reduce the ambiguity of rock mass parameter inversion and improve the real-time performance and accuracy of inversion:

[0073] This invention employs Extended Kalman Filter (EKF) to fuse mechanical parameters, geophysical logging data, and borehole wall visual features to construct a state inversion system that includes uniaxial compressive strength of rock, internal friction angle, and drill bit wear. Through physical mechanism constraints and cross-validation with multi-field data, the ambiguity and multiple solutions caused by single-data inversion are significantly reduced. It can complete real-time estimation of the geological state in front of the drill bit in a very short time, solving the shortcomings of traditional inversion calculations that are slow and unable to capture dynamic evolution processes.

[0074] (3) Realize digital twin-driven advanced simulation and forward-looking control:

[0075] This invention constructs a digital twin model that is synchronized with the physical borehole in real time. It can simulate multiple sets of drilling parameter combinations in parallel in virtual space, and predict the borehole stability, trajectory deviation, water inrush and rock burst risk probability in advance. This transforms drilling decision-making from "passive post-analysis" to "proactive prediction", providing a reliable predictive basis for safe directional drilling construction.

[0076] (4) Form a complete control closed loop of perception-inversion-decision-execution:

[0077] This invention establishes a closed-loop control architecture encompassing downhole sensing, data transmission, surface assimilation, twin inversion, strategy generation, and downhole execution. The system can automatically optimize drilling pressure, rotation speed, drilling fluid discharge, and borehole trajectory based on real-time geological conditions, achieving adaptive, self-learning, and self-regulating intelligent drilling, thereby improving the level of automation and intelligence in construction.

[0078] (5) Panoramic visualization of borehole walls, enabling transparent geological perception:

[0079] Based on the setup of the borehole wall panoramic imaging component, this invention can acquire panoramic images of the borehole wall and quantify visual features such as fracture development density and rock roughness into calculable indicators, upgrading traditional "blind drilling" to "transparent and visible drilling". It can intuitively identify faults, fracture zones, weak interlayers, and water-bearing structures, reducing construction uncertainty. Attached Figure Description

[0080] Figure 1 This is a schematic diagram of the overall layout of the intelligent drilling system in an embodiment of the present invention.

[0081] Figure 2 This is a schematic diagram of the drilling actuator in an embodiment of the present invention.

[0082] Figure 3 This is a flowchart of the intelligent drilling control method in an embodiment of the present invention.

[0083] Figure 1 The markings are as follows: 120 represents surface drilling equipment; 200 represents the underground working environment; 210 represents the drill string / drill rod assembly; 220 represents sandstone layer; 230 represents clay layer; 240 represents coal seam; and 250 represents shale layer.

[0084] Figure 2 The markings are as follows: 1 is the drill bit; 2 is the rotary steering system; 3 is the integrated torque and drill pressure strain gauge; 4 is the gamma ray sensor; 5 is the borehole wall panoramic imaging component; 6 is the data cable; 7 is the multi-source sensing section; and 8 is the transmission interface module. Detailed Implementation

[0085] This invention aims to provide an intelligent directional drilling system and control method based on digital twins and multi-source information fusion, addressing the problems of heterogeneous data silos, delayed parameter inversion solutions, passive risk assessment, and lack of closed-loop intelligent control in existing directional drilling technologies. Its core idea is to use digital twins as the core of virtual-real mapping, and multi-source information spatiotemporal assimilation and fusion inversion as technical support, to construct an integrated intelligent directional drilling system encompassing transparent perception, precise inversion, advanced extrapolation, and closed-loop control. Specifically, by using an integrated multi-source sensing sub in the drill string, three types of heterogeneous data—drilling mechanics, geophysical logging, and borehole panoramic vision—are acquired simultaneously, enabling a three-dimensional perception of drilling conditions and formation status. Through unified spatiotemporal benchmarks, depth lag correction, data vectorization, and visual feature dimensionality reduction, spatiotemporal misalignment and heterogeneity of multi-source data are eliminated, forming standardized, fusion-compatible data. A digital twin is constructed based on rock fracturing dynamics. Extended Kalman filtering is used to fuse multi-source data, inverting rock mechanics parameters and drill bit status to achieve high-fidelity synchronization between physical and digital spaces. Based on the real-time updated digital twin model, advanced operating condition simulations are performed, and multiple drilling strategies are extrapolated in parallel. The optimal control strategy is output by comprehensively considering efficiency, stability, and risk indicators. This optimal strategy is then converted into executable commands and sent to the downhole drilling actuators, forming real-time closed-loop control.

[0086] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0087] Example:

[0088] This embodiment first provides a directional drilling intelligent drilling system based on digital twin and multi-source information fusion, which includes a downhole sensing and execution unit, a data transmission network and a ground intelligent decision-making platform;

[0089] The downhole sensing and execution unit is used to collect multi-source heterogeneous data during the drilling process in real time and execute drilling control commands.

[0090] The data transmission network is used to upload multi-source heterogeneous data collected by the downhole sensing and execution unit to the ground intelligent decision-making platform, and to send control commands generated by the ground intelligent decision-making platform to the downhole sensing and execution unit;

[0091] The ground-based intelligent decision-making platform is used to assimilate multi-source heterogeneous data, update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit, use the digital twin model to perform working condition simulation based on the inverted geological conditions in front of the drill bit, generate the optimal drilling control strategy based on the simulation results, and send the corresponding control commands to the downhole sensing and execution unit through the data transmission network.

[0092] In one exemplary implementation, the overall layout of the above system in an actual construction environment is as follows: Figure 1As shown, System 100 is divided into three core levels in terms of spatial layout and functional logic:

[0093] 1. Ground control unit (i.e., intelligent decision-making platform):

[0094] The platform is the "brain" of the entire drilling system, responsible for processing multidimensional heterogeneous data and issuing control commands; it includes an operation terminal / human-computer interaction interface, an AI decision engine / processing module, and a digital twin display module.

[0095] Operating terminal / human-machine interface: Operators can use this interface to monitor the three-dimensional virtual borehole model and logging-while-drilling charts in real time, enabling them to have a visual understanding of complex working conditions.

[0096] AI Decision Engine / Processing Module: Performs spatiotemporal alignment, standardization, and fusion of drilling parameters, borehole wall visual information, and logging data collected downhole; based on deep learning and rock fracture mechanics, it performs real-time reasoning on drilling conditions, automatically assesses current geological risks (such as stuck drill bit, borehole wall instability), and generates the optimal control strategy.

[0097] Digital twin display module: Through high-fidelity digital mirroring technology, the drilling dynamics (depth, angle, trajectory) in the physical space are mapped to the digital space in real time, achieving synchronization between the virtual and real worlds.

[0098] 2. Surface drilling equipment 120 and transmission network:

[0099] Power and Execution System: The surface drilling rig serves as the power source, transmitting rotational speed and drilling pressure to the downhole via the drill string / drill rod assembly 210.

[0100] Two-way data transmission network: Utilizing mud pulse telemetry and fiber optic interface modules, it enables the uplink transmission of large-capacity sensing data (such as borehole wall images) and the downlink execution of ground decision-making commands (such as changing the drill bit trajectory).

[0101] 3. Underground working environment 200:

[0102] The drilling path traverses multiple geological units, with a typical rock layer interaction: the drill string / drill rod assembly 210 sequentially passes through the sandstone layer 220, the clay layer 230, the coal seam 240, and the shale layer 250.

[0103] Addressing geological uncertainties: The system uses digital means to achieve transparent perception of nonlinear resistance changes caused by alternating rock strata and fault fracture zones.

[0104] In one exemplary implementation, the structure of the downhole sensing execution unit is as follows: Figure 2 As shown, it includes:

[0105] Drill Bit 1: Typically a polycrystalline diamond composite (PDC) drill bit, located at the very tip, responsible for efficiently shearing rock. Its working condition (such as wear) directly affects the rate of penetration (ROP).

[0106] Rotary steering system 2: Following the drill bit, it receives ground commands and dynamically adjusts the drill bit's deflection. It solves the problem of borehole inclination control when drilling through geological structures such as faults.

[0107] Integrated Torque and Drill Pressure Strain Gauge 3: Used to acquire one-dimensional time-series data of drill pressure (WOB) and torque (Torque) in real time; these data are key to constructing nonlinear observation equations and inverting the uniaxial compressive strength (UCS) of rocks.

[0108] Gamma-ray sensor 4: Used to detect the natural radioactivity of strata and assist in determining stratigraphic boundaries, such as identifying the contact relationship between sandstone and coal seams.

[0109] Borehole Wall Panoramic Imaging Component 5: It features a high-pressure transparent sapphire window, which can withstand the harsh underground high-pressure environment. It also has four built-in miniature cameras and an LED array, enabling it to acquire distortion-free panoramic images of the borehole wall.

[0110] The aforementioned integrated torque and drill pressure strain gauge 3, gamma ray sensor 4, and borehole wall panoramic imaging component 5 are integrated into the multi-source sensing section 7; the signal output is connected to the transmission interface module 8 via the data cable 6, and the collected data is transmitted to the ground-based intelligent decision-making platform through the transmission interface module 8 based on mud pulse telemetry technology.

[0111] Based on the above system, the implementation process of the intelligent drilling control method for directional drilling based on digital twin and multi-source information fusion provided in this embodiment is as follows: Figure 3 It includes the following implementation steps:

[0112] S1. Real-time acquisition of multi-source heterogeneous data during the drilling process;

[0113] In this step, the collected multi-source heterogeneous data mainly includes drilling condition data, geophysical response data, and borehole wall panoramic visual data. Drilling condition data, such as drill pressure, rotational speed, torque, and drilling fluid discharge, reflects the drilling operation status. Geophysical response data, such as natural gamma logging values ​​and formation resistivity, reflects lithological characteristics. Borehole wall visual data visually reflects the fracture and fracturing state. These three types of data complement each other, enabling a comprehensive and accurate characterization of drilling conditions and geological conditions.

[0114] S2. Assimilate multi-source heterogeneous data;

[0115] In this step, to address the spatiotemporal misalignment between high-frequency one-dimensional time-series data (drilling parameters), low-frequency two-dimensional image data (hole wall vision), and geophysical data with depth lag (well logging curves), and to provide high-fidelity state input for the digital twin model, this embodiment provides a data assimilation framework based on spatiotemporal alignment and hybrid filtering:

[0116] S21. Unified Spatiotemporal Standard:

[0117] Because different sensors have different sampling frequencies and their installation locations have axial distance differences, a unified spatiotemporal index needs to be established: using a high-precision master clock as the reference time axis. The current depth of the drill bit is used as the spatial axis. This maps multi-source heterogeneous data to the same time-depth coordinate system.

[0118] S22. Depth hysteresis correction:

[0119] For installation behind the drill bit The first meter A sensor (such as a gamma probe). Indicates the first The distance from each sensor to the drill bit, in The data collected in real time actually reflects depth. The geological information at this location necessitates depth lag correction. By establishing a dynamic buffer queue, data collected by sensors located behind the drill bit is forward-corrected and mapped to the drill bit's current actual contact depth. .

[0120] S23. Heterogeneous data vectorization:

[0121] Drilling condition data and geophysical response data were converted into standardized numerical vectors respectively:

[0122] Drilling condition vector at time step , represented as ; For drilling pressure; Rotational speed; Torque; This refers to the drilling fluid discharge rate; This represents the matrix transpose operation.

[0123] depth Geophysical vector at location , represented as ; Natural gamma logging value; Formation resistivity; This represents the matrix transpose operation.

[0124] S24. Dimensionality reduction of visual features of hole wall images:

[0125] Since the raw visual data is too large to be directly input into a numerical model, this embodiment uses a lightweight convolutional neural network (CNN) to process the distortion-corrected panoramic image of the hole wall in real time.

[0126] Image segmentation and texture analysis: The CNN model segments the image into rock matrix region, fracture region and broken region.

[0127] Feature extraction: Output two key quantitative indicators:

[0128] : Crack development density index;

[0129] : Hole wall roughness texture index.

[0130] Based on the above two indicators, a visual feature vector is formed. , represented as , This represents the matrix transpose operation.

[0131] S3. Update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit;

[0132] In this step, assimilated multi-source data is used to achieve joint constraint inversion. An extended Kalman filter is employed to correct the state, suppressing noise and reducing inversion ambiguity. The inversion results are then synchronized to the digital twin model to ensure high-fidelity synchronization between the virtual model and the physical drilling process. The specific implementation of this step is as follows:

[0133] S31. Define the digital twin state vector :

[0134] ;in, It represents the uniaxial compressive strength of the rock. The internal friction angle of the rock; This refers to drill bit wear. Represents the matrix transpose operation;

[0135] The above digital twin state vector This is the core objective that requires inversion to solve.

[0136] S32. Establish the state prediction equation:

[0137] Based on the dynamic mechanism of rock fracturing, and according to the state at the previous moment and the current drilling condition vector Predict the prior estimate of the state at the current moment. :

[0138] ;in, This is a nonlinear rock fracturing dynamics function; This refers to the process noise from the previous moment;

[0139] S33. Constructing multi-source fusion observation vectors and observation equations:

[0140] The assimilated mechanical data, geophysical data, and visual feature data are uniformly constructed into an observation vector. :

[0141] ;

[0142] in, for Torque at any given moment; for Mechanical drilling speed at any given moment; For depth Geophysical vector at the location; For depth Visual feature vector at the location; Represents the matrix transpose operation;

[0143] Establish a nonlinear mapping relationship between the state vector and the observation vector, and construct the observation equation:

[0144] ;

[0145] in, It is a nonlinear observation function; To observe noise;

[0146] S34. Use a filtering algorithm for state correction to achieve parameter inversion:

[0147] Calculate Kalman gain Using observation vectors Compared with predicted observations The residuals between the two conditions are used to correct the prior state estimate, resulting in the optimal posterior geological state estimate. :

[0148] ;

[0149] S35. Real-time synchronous updating of the digital twin model: This involves updating the obtained posterior geological state estimate. By incorporating the digital twin model, the geological conditions, rock mechanics parameter distribution, and drilling tool working status of the virtual borehole are updated.

[0150] S4. Based on the geological conditions ahead of the drill bit derived by inversion, the working conditions are simulated using a digital twin model, and the optimal drilling control strategy is generated based on the simulation results.

[0151] In this step, the stability of the borehole wall, trajectory deviation, and risk probability under multiple sets of drilling parameter combinations are simulated in parallel within a digital twin model. This allows for the advance prediction of borehole wall stability, trajectory control accuracy, and disaster risk probability for different parameters without affecting actual construction. Borehole wall stability includes borehole wall stress distribution and collapse risk index under different drilling parameters, which can serve as a basis for deciding whether to reduce drilling pressure or rotational speed. Trajectory deviation, based on formation anisotropy and fault location, predicts trajectory deviation trends under different guidance controls, serving as a basis for dynamically correcting the drilling direction. Disaster risk probability includes borehole wall instability, fracture zones, high-permeability areas, and water inrush risk levels, used to determine whether proactive risk avoidance, pump rate adjustment, or drilling rhythm changes are necessary. Based on the above multi-objective comprehensive optimization, the parameter combination that optimizes mechanical drilling speed and minimizes drill bit wear is selected under the premise of ensuring safety and stability, thus obtaining the optimal drilling control strategy.

[0152] S5. Generate corresponding control commands based on the optimal drilling control strategy and issue them to the drilling actuator.

[0153] In this step, the intelligent decision-making and control module in the ground intelligent decision-making platform converts the strategy into control commands, which are then transmitted to the downhole drilling actuators via the data transmission network to complete real-time closed-loop control.

[0154] Although embodiments of the present invention have been described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present invention, and all such changes and alterations shall not depart from the protection scope of the present invention.

Claims

1. A directional drilling intelligent drilling system based on digital twin and multi-source information fusion, characterized in that, include: Downhole sensing and execution unit, data transmission network, and surface intelligent decision-making platform; The downhole sensing and execution unit is used to collect multi-source heterogeneous data during the drilling process in real time and execute drilling control commands. The data transmission network is used to upload multi-source heterogeneous data collected by the downhole sensing and execution unit to the ground intelligent decision-making platform, and to send control commands generated by the ground intelligent decision-making platform to the downhole sensing and execution unit; The ground-based intelligent decision-making platform is used to assimilate multi-source heterogeneous data, update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit, use the digital twin model to perform working condition simulation based on the inverted geological conditions in front of the drill bit, generate the optimal drilling control strategy based on the simulation results, and send the corresponding control commands to the downhole sensing and execution unit through the data transmission network.

2. The intelligent directional drilling system based on digital twin and multi-source information fusion as described in claim 1, characterized in that, The downhole sensing and execution unit is integrated at the end of the drill string assembly and includes a multi-functional measurement while drilling module, a borehole wall panoramic imaging component, and a drilling execution mechanism. The multi-functional measurement while drilling module is used to collect drilling mechanical parameters and geophysical logging data; The borehole wall panoramic imaging component is used to acquire panoramic image data of the borehole wall; The drilling actuator is used to receive and execute drilling control commands.

3. The intelligent directional drilling system based on digital twin and multi-source information fusion as described in claim 1 or 2, characterized in that, The ground-based intelligent decision-making platform includes a data assimilation module, a digital twin model module, and an intelligent decision-making and control module; The data assimilation module is used to perform spatiotemporal alignment of multi-source heterogeneous data; The digital twin model module is used to update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit. Based on the inverted geological conditions in front of the drill bit, the digital twin model is used to perform working condition simulation. The intelligent decision-making and control module is used to generate the optimal drilling control strategy based on the deduction results of the digital twin model, and to issue corresponding control commands to the drilling execution mechanism.

4. A method for intelligent directional drilling control based on digital twin and multi-source information fusion, applied to the intelligent directional drilling system based on digital twin and multi-source information fusion as described in any one of claims 1 to 3, characterized in that, The method includes the following steps: S1. Real-time acquisition of multi-source heterogeneous data during the drilling process; S2. Assimilate the multi-source heterogeneous data; S3. Update the digital twin model based on the assimilated data and invert the geological conditions in front of the drill bit; S4. Based on the geological conditions ahead of the drill bit derived by inversion, the working conditions are simulated using a digital twin model, and the optimal drilling control strategy is generated based on the simulation results. S5. Generate corresponding control commands based on the optimal drilling control strategy and issue them to the drilling actuator.

5. The intelligent drilling control method for directional drilling based on digital twin and multi-source information fusion as described in claim 4, characterized in that, In step S1, the multi-source heterogeneous data includes: drilling condition data, geophysical response data, and borehole wall panoramic visual data; the drilling condition data includes drilling pressure, rotation speed, torque, and drilling fluid discharge; the geophysical response data includes natural gamma logging values ​​and formation resistivity.

6. The intelligent drilling control method for directional drilling based on digital twin and multi-source information fusion as described in claim 4, characterized in that, In step S2, the assimilation process for the multi-source heterogeneous data includes: S21. Unified time and space reference: using a high-precision master clock as the reference time axis. The current depth of the drill bit is used as the spatial axis. This maps multi-source heterogeneous data to the same time-depth coordinate system. S22. Depth Hysteresis Correction: Establish a dynamic buffer queue to forward-correct and map data collected by sensors located behind the drill bit to the current actual contact depth of the drill bit. ; S23. Heterogeneous Data Vectorization: Convert drilling condition data and geophysical response data into standardized numerical vectors respectively. Drilling condition vector at time step , represented as ; For drilling pressure; Rotational speed; Torque; This refers to the drilling fluid discharge rate; Represents the matrix transpose operation; depth Geophysical vector at location , represented as ; Natural gamma logging value; Formation resistivity; Represents the matrix transpose operation; S24. Dimensionality Reduction of Visual Features in Hole Wall Images: A lightweight convolutional neural network (CNN) is used to segment and extract features from panoramic images of hole walls, outputting a fracture development density index. Texture index of hole wall roughness To form visual feature vectors , represented as , This represents the matrix transpose operation.

7. The intelligent drilling control method for directional drilling based on digital twin and multi-source information fusion as described in claim 6, characterized in that, In step S3, the method of updating the digital twin model and inverting the geological conditions in front of the drill bit based on the assimilated data includes: S31. Define the digital twin state vector : ;in, It represents the uniaxial compressive strength of the rock. The internal friction angle of the rock; This refers to drill bit wear. Represents the matrix transpose operation; S32. Establish the state prediction equation: Based on the dynamic mechanism of rock fracturing, and according to the state at the previous moment and the current drilling condition vector Predict the prior estimate of the state at the current moment. : ;in, This is a nonlinear rock fracturing dynamics function; This refers to the process noise from the previous moment; S33. Constructing multi-source fusion observation vectors and observation equations: The assimilated mechanical data, geophysical data, and visual feature data are uniformly constructed into an observation vector. : ； in, for Torque at any given moment; for Mechanical drilling speed at any given moment; For depth Geophysical vector at the location; For depth Visual feature vector at the location; Represents the matrix transpose operation; Establish a nonlinear mapping relationship between the state vector and the observation vector, and construct the observation equation: ； in, It is a nonlinear observation function; To observe noise; S34. Use a filtering algorithm for state correction to achieve parameter inversion: Calculate Kalman gain Using observation vectors Compared with predicted observations The residuals between the two conditions are used to correct the prior state estimate, resulting in the optimal posterior geological state estimate. : ； S35. Real-time synchronous updating of the digital twin model: This involves updating the obtained posterior geological state estimate. By incorporating the digital twin model, the geological conditions, rock mechanics parameter distribution, and drilling tool working status of the virtual borehole are updated.

8. The intelligent drilling control method for directional drilling based on digital twin and multi-source information fusion as described in claim 4, characterized in that, In step S4, the working condition simulation includes: simulating in parallel the hole wall stability, trajectory deviation and risk probability under multiple sets of drilling parameter combinations in a digital twin model.

9. The intelligent drilling control method for directional drilling based on digital twin and multi-source information fusion as described in claim 4, characterized in that, In step S4, the optimal drilling control strategy includes: It adaptively adjusts drilling pressure, rotation speed, and drilling fluid discharge, dynamically corrects the borehole trajectory, and actively suppresses and controls borehole instability, fracture zones, and water inrush risks.

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