Directional drilling system and method based on muon navigation and distributed fiber sensing

Abstract

The application belongs to the technical field of directional intelligent drilling, and provides a directional drilling system and method based on muon navigation and distributed optical fiber sensing, which comprises a muon navigation and detection module, which is used for collecting energy loss and scattering signals when muon rays pass through the stratum above drilling, positioning a directional drilling bit according to the signals, and generating a surrounding rock density distribution image; a distributed optical fiber sensing module, which is used for analyzing changes in optical signals during drilling, and determining various monitoring environmental variables around the drill pipe during drilling; a data fusion and control system, which is used for matching and fusing the generated surrounding rock density distribution image and various physical monitoring variables around the drill pipe during drilling, obtaining drilling fusion data, and generating a drilling path and effect optimization strategy based on the drilling fusion data. The application can effectively cope with complex underground environments, provide more accurate geological information, optimize the drilling process, and reduce potential risks.

Description

Technical Field

[0001] This invention belongs to the field of directional intelligent drilling technology, and particularly relates to a directional drilling system and method based on muon navigation and distributed optical fiber sensing. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Directional drilling plays a crucial role in tunnel geological prediction, particularly in detecting complex geological structures and water-bearing formations, and even in fields such as oil extraction and coal exploration. Current directional drilling technologies primarily employ measurement-while-drilling (MWD) and logging-while-drilling (LOD) systems. These systems integrate sensors such as gyroscopes, accelerometers, and magnetometers to achieve real-time monitoring of the drill bit's attitude, azimuth, and trajectory. Simultaneously, they utilize logging methods such as electromagnetic waves, resistivity, or acoustic waves to indirectly invert formation information. In the field of tunnel geological prediction, ground-penetrating radar (GPR), seismic wave reflection, and borehole television are also frequently combined to detect unfavorable geological formations ahead.

[0004] However, electromagnetic and magnetic methods are susceptible to interference from underground highly conductive water-bearing bodies and metallic environments, resulting in a significant decrease in measurement accuracy in water-rich fault zones or environments with strong electromagnetic shielding. Existing detection methods mostly acquire information from a single physical field, and their ability to identify complex structures (such as fracture zones, karst caves, and water-bearing structures) is limited. In high-pressure, water-rich, and strongly disturbed environments, the stability and anti-interference capabilities of the sensing system are insufficient, which restricts the accuracy and safety of directional drilling. Summary of the Invention

[0005] To address at least one of the technical problems existing in the background art, the first aspect of the present invention provides a directional drilling system based on muon navigation and distributed optical fiber sensing, which can effectively improve the positioning accuracy and data transmission stability of drilling operations, effectively cope with complex underground environments, provide more accurate geological information, optimize the drilling process, and reduce potential risks.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A directional drilling system based on muon navigation and distributed fiber optic sensing includes: The muon navigation and detection module is used to collect energy loss and scattering signals when muon rays pass through the strata above the drill bit, locate the directional drilling bit based on the signals, and generate a surrounding rock density distribution image. The distributed fiber optic sensing module is used to analyze changes in optical signals during drilling and determine various environmental variables around the drill pipe during the drilling process. The data fusion and control system is used to match and fuse generated surrounding rock density distribution images with various physical monitoring quantities around the drill pipe during drilling to obtain drilling fusion data, and to generate drilling paths and effect optimization strategies based on the drilling fusion data.

[0007] In one implementation, the muon navigation and detection module includes a muon detection array, which comprises a ground reference array and a drilling-while-drilling (DWDD) array. The ground reference array consists of multiple controllable muon detection array units or controllable artificial muon sources, used to capture the incident energy and angle information of muon rays on the ground, or to generate high-energy, high-flux muon beams. The DWDD array is used to capture muon ray signals penetrating the upper strata during drilling in real time.

[0008] As one implementation method, the arrangement of the ground reference array is set according to the geometric layout of the detection scenario. When the detection scenario is a linear project or a structure with axial extension characteristics, a linear layout is adopted. When the detection scene is a planar distribution or a large-scale regional detection, a grid layout is adopted; When the detection scenario involves detecting key local targets or requires high-precision inversion of the central area, a ring layout is adopted.

[0009] As one implementation method, the spacing between the detector units in the muon detector array is determined comprehensively based on the muon flux characteristics, the target size, and the required spatial resolution.

[0010] As one implementation method, the muon navigation and detection module locates the directional drilling bit based on the signal, specifically including: inverting the muon's crossing path based on the time difference, energy attenuation and scattering information between the muon on the ground reference array and the drilling moving array, and calculating the spatial position and attitude parameters of the drilling moving array or the drill bit accordingly.

[0011] As one implementation method, in the distributed optical fiber sensing module, the demodulation processing of the backscattered light signal collected by the distributed optical fiber sensor includes: performing coherent detection on the continuously acquired Rayleigh scattering echo to obtain the optical field phase information; extracting the peak frequency shift by scanning or fitting the Brillouin gain spectrum to invert the temperature and strain distribution; and calculating the temperature distribution along the optical fiber based on the relationship between the intensity ratio of Raman anti-Stokes light and Stokes light and temperature.

[0012] As one implementation method, the matching and fusion of the generated surrounding rock density distribution image and various physical monitoring quantities around the drill pipe during drilling includes: The image data of surrounding rock density distribution and various physical monitoring quantities are standardized and normalized. Among them, the various physical monitoring quantities include temperature, strain and vibration signals. Based on the coupling relationship between surrounding rock density and temperature, strain and vibration, a fusion identification model is constructed. The fusion identification model adopts a weighted fusion model or a probabilistic fusion model. After fusion of the identification model, one or more of the following are obtained: three-dimensional fused physical property field, anomaly probability distribution map, and drilling risk assessment index.

[0013] As one implementation method, the weighted fusion model is expressed as:

[0014] in, To fuse feature parameters, These are weighting coefficients, which can be determined based on experience or training data. , , and This represents the density distribution, temperature, strain, and vibration signals of the surrounding rock after coordinate unification. The probabilistic fusion model uses the Bayesian method, and is expressed as follows: , in, It indicates abnormal geological bodies (such as water-rich areas, cavities, etc.). For observation data, Represents the posterior probability. Represents the likelihood function. This represents the prior probability.

[0015] As one implementation method, in a data fusion and control system, drilling paths and performance optimization strategies are generated based on fused drilling data, including: Based on drilling fusion data, density anomaly gradient, temperature change rate, strain concentration degree and comprehensive risk indicators are extracted; A three-tiered progressive triggering mechanism is established based on comprehensive risk indicators and set thresholds. When the conditions are met... and When the surrounding rock conditions ahead are deemed favorable, the control module maintains the current drilling path and parameters, continuously monitors, and does not trigger path optimization. When satisfied but If a potential anomaly is detected ahead but the risk is still within a controllable range, the control module triggers a local fine-tuning strategy, adjusting the drilling speed without changing the main path direction. Drilling pressure and speed Make small adaptive adjustments to smoothly transition through abnormal regions; When satisfied When a high-risk anomalous geological body is detected ahead, the control module initiates global path replanning, triggering an optimization process. This process includes: transforming the path planning problem into a constrained optimization problem; minimizing the drilling cost function while ensuring safety; and adjusting the path based on real-time fused data characteristics, density anomaly gradient, temperature change rate, strain concentration, comprehensive risk indicators, and set path adjustment decisions, based on the already planned path.

[0016] To address the aforementioned problems, a second aspect of this invention provides a directional drilling method based on muon navigation and distributed optical fiber sensing, which can effectively improve the positioning accuracy and data transmission stability of drilling operations, effectively cope with complex underground environments, provide more accurate geological information, optimize the drilling process, and reduce potential risks.

[0017] To achieve the above objectives, the present invention adopts the following technical solution: The directional drilling method based on muon navigation and distributed fiber optic sensing includes the following steps: The energy loss and scattering signals of muon rays passing through the strata above the drill bit are collected. The directional drilling bit is located based on the signals, and a density distribution image of the surrounding rock is generated. The backscattered light signal collected by the distributed optical fiber sensor is demodulated, and physical parameters distributed along the optical fiber are extracted. The changes in the light signal during the drilling process are analyzed in combination with the extracted physical parameters to determine the various environmental variables monitored around the drill pipe during the drilling process. The generated surrounding rock density distribution image and various physical monitoring quantities around the drill pipe during drilling are matched and fused to obtain drilling fusion data. Drilling path and effect optimization strategy are generated based on the drilling fusion data.

[0018] The beneficial effects of this invention are: This invention can generate a drilling surrounding rock density distribution cloud map based on information such as the energy attenuation and scattering of muons in the formation, and match and fuse it with information such as stress and strain and temperature changes around the drill pipe monitored by a distributed optical fiber sensing system to generate a comprehensive real-time drilling monitoring model with information such as geological structure, drilling status and environmental conditions; track drilling progress and equipment status, automatically optimize the drilling path and intelligently adjust drilling parameters.

[0019] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 This is a schematic diagram of a directional intelligent drilling system based on muon navigation and distributed optical fiber sensing provided in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of the muon navigation method provided in this embodiment of the invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] In this invention, terms such as "connected" and "linked" should be interpreted broadly, indicating a fixed connection, an integral connection, or a detachable connection; a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can determine the specific meaning of these terms in this invention based on the specific circumstances, and they should not be construed as limitations on the invention.

[0026] like Figure 1 As shown, as an embodiment of the present invention, this embodiment provides a directional intelligent drilling system based on muon navigation and distributed optical fiber sensing, comprising: The muon navigation and detection system is used to collect energy loss and scattering signals of muon rays as they pass through the strata above the drill bit. Based on these signals, the system performs high-precision positioning and navigation of the directional drilling bit and generates images of the surrounding rock density distribution. A distributed optical fiber sensing system is used to transmit communication data and optical signals during the drilling process. By analyzing changes in the optical signals, various physical parameters such as temperature changes and strain distribution along the optical fiber during the drilling process can be monitored in real time. The data fusion and control system is used to fuse muon navigation data and distributed fiber optic sensing data in real time and generate a comprehensive drilling model and real-time drilling status report. It can also adjust the drilling direction and speed in real time to adapt to different geological conditions and improve drilling accuracy and efficiency.

[0027] The muon navigation and detection system includes: like Figure 2 As shown, the muon detection array includes a ground reference array and a moving array while drilling. The ground reference array consists of multiple controllable muon detection array units or controllable artificial muon sources, used to capture information such as the incident energy and angle of muon rays on the ground, or to generate high-energy, high-flux muon beams. The moving array while drilling is used to capture muon ray signals penetrating the upper strata in real time during drilling, which helps to analyze the path and distribution characteristics of muons passing through the interior of the strata, and thus infer changes in the underground structure and the distribution of strata. As a further embodiment, the arrangement of the ground reference array is geometrically configured according to the detection scenario, and can adopt a straight line, grid or ring to maximize the detection range; When the detection scenario is a linear project or a structure with obvious axial extension characteristics (such as tunnel advanced geological prediction, underground pipe gallery, directional drilling along the line detection, etc.), a linear layout is preferred, with a reference array set up along the project axis to enhance the directional resolution of the target area ahead and improve the identification accuracy of anomalies such as fault fracture zones and water-bearing structures. When the detection scenario is a planar distribution or a regional detection with a large spatial range (such as detection of mining goaf, advance prediction of coal mine working face, identification of oil and gas reservoir range, etc.), a grid layout is preferred. By uniformly deploying a two-dimensional reference array, full coverage scanning of the target area can be achieved, improving spatial resolution and imaging integrity. When the detection scenario involves the detection of key local targets or the need for high-precision inversion of the central area (such as the location of karst caves, underground cavities, water-rich anomaly areas, or high-risk geological bodies), a ring layout is preferred, with a reference array deployed around the target area to enhance the multi-angle information acquisition capability of the central area and improve imaging accuracy and inversion stability.

[0028] Determine the appropriate spacing between each detector element in the detector array based on the muon flux and the required spatial resolution; As a further embodiment, the spacing between the detection units is determined comprehensively based on the muon flux characteristics, target size, and required spatial resolution, specifically as follows: First, based on the attenuation law of cosmic ray muons in underground media, the effective muon flux received by the detection unit is negatively correlated with the detection path length and the media density. When the burial depth is large or there are high-density anomalies (such as water-rich areas, fracture zones, etc.), the muon flux is significantly reduced. To ensure that the detection data has sufficient statistical reliability, it is necessary to appropriately reduce the spacing between detection units to increase the number of samples per unit area, thereby improving the signal-to-noise ratio.

[0029] Secondly, the spacing between detection units should meet the spatial resolution requirements of the target. Typically, the minimum feature size L of the target anomaly is used as the control scale, and the spacing d between detection units should preferably satisfy d ≤ (0.5). 1.0)L, to ensure that the boundary features and internal structural changes of the target can be effectively identified; for high-precision detection requirements, d can be further taken to ensure that.

[0030] Furthermore, based on the statistical characteristics of muon counting, the count value of the detection unit per unit time must meet the preset statistical accuracy requirements. According to the Poisson statistical law, when the count value N satisfies... At this time, relatively stable statistical results can be obtained. Therefore, in regions with low muon flux, the cell count level should be increased by reducing the spacing between detector cells or extending the acquisition time, thereby meeting the inversion accuracy requirements.

[0031] In addition, engineering deployment conditions and economic factors must be comprehensively considered. While ensuring detection accuracy, the relationship between the spacing and deployment density of detection units should be optimized to avoid redundant sensor deployment and improve the overall system efficiency.

[0032] In summary, the spacing between detection units is the result of coupled optimization of multiple factors such as muon flux, target size, statistical accuracy, and engineering conditions, and can be adaptively adjusted according to specific detection scenarios.

[0033] The height and angle of the detection units are adjusted to adapt to the expected muon incident angle; a high-precision clock system is used to ensure time synchronization of all detection units; an efficient data acquisition and transmission system is equipped to ensure that data is transmitted to the processing center in real time and accurately; different arrangement schemes are tested through computer simulation to optimize the array design; and the ground reference array is ensured to provide the muon navigation system with accurate timestamps and spatial positioning, thereby achieving efficient and reliable navigation performance.

[0034] The signal acquisition module is used to acquire and analyze the scintillation signal, which contains information such as energy loss and scattering, generated when muon rays pass through a plastic scintillator. Specifically, the signal acquisition module includes a photomultiplier tube and a clock synchronization device; Photomultiplier tubes are used to collect scintillation signals, including information on the energy attenuation and scattering of muons in underground media. At the same time, photomultiplier tubes can amplify and enhance the weak signals generated by plastic scintillators to form measurable electrical signals. A clock synchronization device is used to record the precise time of muon signal acquisition and ensure clock synchronization of all detection units to improve the measurement accuracy of the system.

[0035] This module uses, but is not limited to, high-precision clock sources, such as chip-scale atomic clocks (CSAC), to provide a unified time reference for the entire system; the signal acquisition module transmits the information to the positioning calculation module 1013 and the data fusion module, etc. The positioning calculation module is used to invert the crossing path of the muon based on the flight time difference, energy attenuation and scattering information between the ground reference array and the drilling moving array, and to calculate the spatial position and attitude parameters of the drilling moving array or the drill bit accordingly. As a further embodiment, the time difference of flight, energy attenuation, and scattering information are used as joint constraints. Weighted least squares or Bayesian inversion methods are introduced to iteratively optimize the position and attitude parameters in order to improve the positioning accuracy and stability in complex geological environments.

[0036] As a further implementation method, flight time difference , and The ground reference array and the moving array while drilling record the trigger time of the same muon event; As a further implementation, the energy loss of muons in the medium satisfies an approximate relationship: , in, Indicates ionization energy loss. Let represent the radiation loss term. Integrating over the path yields the muon penetration thickness (i.e., the product of path length and density): , in, Indicates path density, Indicates the path length. , Indicates the speed of the muon. , , For Miao Zijing's quality, For Muzi energy, and These are the incident and emitted energies, respectively. By measuring or estimating the changes in muon energy, constraints can be imposed on the length of its traversal path and the distribution of the medium it passes through.

[0037] Muons undergo multiple Coulomb scattering during their passage through a medium, with the root mean square of the scattering angle satisfying: , in, The root mean square scattering angle is... For momentum, , The distance is the radiation length of the medium. By measuring the incident and exit directions of the muons, their actual paths can be corrected, improving the accuracy of trajectory inversion. Using the above method, the positioning calculation module can achieve high-precision three-dimensional positioning and attitude inversion of the drilling moving array or drill bit under electromagnetic dependence, which is particularly suitable for directional drilling operations in high-pressure, water-rich, and strong electromagnetic interference environments.

[0038] The distributed optical fiber sensing system includes: Distributed optical fiber is used to transmit communication data and optical signals during drilling. It also serves as part of a sensing system to monitor physical parameters such as temperature and strain along the fiber optic route. This enables real-time, bidirectional, high-capacity data transmission between the muon navigation and detection system and the data fusion and control system. The signal demodulation module is used to demodulate the backscattered light signal collected by the distributed optical fiber sensor, extract the physical parameters distributed along the optical fiber, and monitor the physical parameters such as temperature and strain around the drill pipe based on the changes of the optical signal in the optical fiber during data transmission. As a further embodiment, the signal demodulation module is used to demodulate the backscattered light signal collected by the distributed optical fiber sensor and extract physical parameters such as temperature and strain distributed along the optical fiber. The specific implementation process is as follows: A narrow pulse of light is injected into an optical fiber using a pulsed laser source. During propagation, the light interacts with the microscopic inhomogeneities within the fiber, generating backscattered signals such as Rayleigh scattering, Brillouin scattering, and Raman scattering. The signal demodulation module receives the backscattered light using a high-speed photodetector and records its intensity over time. .

[0039] In this embodiment, the demodulation processing of the backscattered light signal includes demodulation based on Rayleigh scattering (DAS / OTDR), demodulation based on Brillouin scattering (BOTDA / BOTDR), and demodulation based on Raman scattering (DTS). Specifically, during Rayleigh scattering (DAS / OTDR) demodulation, optical field phase information is obtained by coherently detecting continuously acquired Rayleigh scattering echoes, and dynamic strain or vibration signals can be obtained through phase difference processing. When demodulating based on Brillouin scattering (BOTDA / BOTDR), temperature and strain distribution can be inverted by extracting the peak frequency shift through scanning or fitting the Brillouin gain spectrum. During Raman scattering (DTS) demodulation, the temperature distribution along the optical fiber can be calculated based on the relationship between the intensity ratio of Raman anti-Stokes light to Stokes light and temperature. Through the above demodulation process, the data output by the signal demodulation module includes, but is not limited to: temperature distribution function along the fiber length. Strain distribution function along the fiber length Dynamic vibration or acoustic signals (frequency, amplitude, phase, etc.) and optical signal attenuation characteristics (reflecting local damage or interface changes).

[0040] The above data has a two-dimensional distribution characteristic of "time-space", which can realize continuous monitoring of the entire length of the drill pipe.

[0041] During drilling, changes in the geological environment around the drill pipe will cause the spatiotemporal evolution of the fiber optic signal. The specific analysis method is as follows: Temperature anomaly identification: When the drill bit enters a water-rich area or seepage occurs, the temperature distribution curve changes due to enhanced fluid convection heat transfer. Localized abnormal changes occur (such as sudden temperature drops or gradient changes). A temperature gradient threshold is set: Potentially aquifer structures can be identified, among which, This is the temperature gradient threshold; Strain and surrounding rock disturbance analysis: Drilling disturbance or surrounding rock fracturing can cause abrupt changes in fiber optic strain, manifested as follows: , By identifying abnormal strain ranges, the location of fracture zones or weak structures can be determined. The fiber strain abrupt change threshold; This indicates a sudden change in fiber strain; Vibration characteristic analysis: The vibration spectrum is analyzed using dynamic signals obtained from Rayleigh scattering. ,in This is a Fourier transform. Different geological bodies correspond to different frequency response characteristics, which can be used to identify lithological changes or drilling status.

[0042] A discrimination function is constructed by jointly analyzing temperature, strain, and vibration information. , in, The weighting coefficients are used to comprehensively identify the environment surrounding the drill pipe (such as water-rich areas, cavities, fracture zones, etc.) through threshold discrimination or machine learning methods.

[0043] The signal demodulation module transmits the demodulation and analysis results to the host processing system to generate a multi-physics field distribution map along the drilling direction, enabling real-time dynamic monitoring and early warning of environmental variables during the drilling process.

[0044] Through the above demodulation and analysis methods, quantitative analysis and multi-parameter inversion of optical signals can be achieved, ensuring the monitoring accuracy and reliability of the system in complex underground environments.

[0045] The data transmission module is installed at the drilling front end and the ground control center respectively. It uses fiber optic technology to achieve high-speed and stable data transmission, and transmits the demodulated muon positioning data and fiber optic sensing data to the data fusion and control system to ensure the real-time performance and accuracy of data transmission.

[0046] In some embodiments, distributed optical fibers can be laid along the drill pipe direction to realize optical fiber link connections between each sensor of the drill bit and the ground drilling control center. The optical fiber link can realize high bandwidth and low latency data transmission, enabling data to be transmitted to the ground control center quickly. By analyzing the amplitude, phase or frequency changes of the optical signal, the signal demodulation module can detect changes in environmental conditions in real time and provide real-time data, which helps to respond quickly to environmental changes during the drilling process.

[0047] Muzi navigation technology is unaffected by the Earth's magnetic field and is not easily interfered with or misled by external signals. It has good concealment and stability, and can provide high-precision and high-stability positioning information for directional drilling. Distributed fiber optic sensing technology can monitor multiple parameters such as temperature, strain and vibration, and can also achieve low-latency, high-capacity data transmission, which can provide more comprehensive monitoring information on drilling trajectory, geological environment and other aspects for directional drilling.

[0048] The data fusion and control system includes: The data fusion module is used to match and fuse the surrounding rock density distribution image obtained by muon detection with various physical monitoring quantities around the drill pipe during drilling to generate fused data to guide drilling. The specific implementation process is as follows: (1) Standardization and coordinate unification of multi-source data First, the density distribution image data of the surrounding rock. With fiber optic sensing data (temperature) ,strain Vibration signals ) will be processed uniformly.

[0049] By transforming the coordinates, the position along the fiber optic cable is determined. Mapping to three-dimensional space coordinates: , in, This is a spatial mapping function based on the drill pipe trajectory.

[0050] At the same time, data with different dimensions are normalized to eliminate the influence of dimensions and improve the stability of fusion.

[0051] (2) Spatial matching and interpolation reconstruction Since muon imaging data is spatial volume data, while fiber optic data is one-dimensional distributed data, interpolation methods are needed to extend the fiber optic data to three-dimensional space.

[0052] Use radial basis function (RBF) or inverse distance weighted (IDW) interpolation methods:

[0053] in, , For spatial distance, This is the weighted index.

[0054] This allows us to obtain temperature field, strain field, and vibration field distributions with the same resolution as the density field.

[0055] (3) Construction of multiphysics fusion model Based on the coupling relationship between surrounding rock density and temperature, strain and vibration, a fusion identification model is constructed. In this embodiment, the fusion identification model can be fused using a weighted fusion model or a probabilistic fusion model; The weighted fusion model is expressed as follows:

[0056] in, To fuse feature parameters, These are weighting coefficients, which can be determined based on experience or training data. , , and This represents the density distribution, temperature, strain, and vibration signals of the surrounding rock after coordinate unification. The probabilistic fusion model, expressed using the Bayesian method, is as follows: , in, It indicates abnormal geological bodies (such as water-rich areas, cavities, etc.). For observation data, Represents the posterior probability. Represents the likelihood function. This represents the prior probability.

[0057] Among them, prior probability The determination process includes: Prior probabilities are initially assigned based on previous geological exploration data of the drilling area (such as seismic reflection profiles, core records from adjacent wells, or regional geological maps). The space ahead of the drill is divided into... Each discrete voxel is assigned a... The probability values ​​between, and satisfying For unknown regions without any prior information, a uniform distribution is adopted, assuming... Based on this, a distance attenuation factor is introduced to assign a higher prior weight to the forward voxel closest to the current drill bit in order to reflect geological continuity.

[0058] Likelihood function The construction process includes: The likelihood function is used to quantify the probability of anomalies appearing under current observational data. Assuming that the physical fields are independent, a joint likelihood function is constructed: , The specific calculation model for each component is as follows: density likelihood : Based on the density of the surrounding rock obtained by muon imaging, when the density value Below background density Furthermore, if the difference exceeds the statistical error, it is judged as abnormal.

[0059] Use Gaussian membership functions: , in, This represents the average density of normal surrounding rock. The standard deviation of density measurement is given; low density (such as cavities or water-rich areas) corresponds to a high likelihood value.

[0060] Temperature likelihood Based on distributed fiber optic temperature measurement data. Anomalous geological bodies (such as water-rich fault zones) are often accompanied by abrupt temperature gradient changes caused by thermal convection. The temperature gradient is defined. ,when Exceeding the preset threshold When this happens, the probability of an anomaly increases: , Strain likelihood Based on fiber optic strain monitoring data. Significant strain concentration will occur when the surrounding rock becomes unstable due to drilling disturbances or when crossing fractured zones. A strain anomaly index is defined. ,in Given the reference strain value under normal drilling conditions, its likelihood function is: , in, This is the critical value for sudden strain change.

[0061] Vibrational likelihood Dynamic vibration signals obtained based on Rayleigh scattering. According to the unique frequency response characteristics of different geological bodies (e.g., low-frequency, high-amplitude scattering in water-rich areas and broadband scattering in fractured zones), the correlation coefficient between the vibration spectrum and a preset standard anomaly spectrum template is calculated. To define: , Posterior probability Fusion determination Substituting the prior probabilities and likelihood functions into Bayes' theorem, we can calculate the posterior probability of each spatial voxel.

[0062] The final fusion identification criterion is: when (in The preferred range for the preset alarm threshold is: When the voxel is identified as an anomalous geological body (water-rich area, cavity, or fracture zone), a probability distribution map of the anomalous body is generated simultaneously. After fusion processing, the "drilling fusion data" output by the data fusion module 1031 includes, but is not limited to, the following forms: three-dimensional fused physical property field: such as... Used to characterize the comprehensive properties of surrounding rock (density-temperature-strain coupling characteristics); anomaly probability distribution map: Used to identify water-rich areas, cavities, and fractured zones; Drilling risk assessment indicators: ,in, , and Indicates the weighting coefficient. It represents the temperature gradient (reflecting the rate of temperature change caused by abnormal heat flow or groundwater activity). It represents the strain value (reflecting the degree of deformation of the surrounding rock after being disturbed during drilling). The value represents the density of the surrounding rock (reflecting the compactness and integrity of the formation) and is used to assess drilling safety in real time; the directional control parameters include the drill bit deflection direction, adjustment angle, and propulsion strategy recommendations.

[0063] By using the above fusion method, the "macroscopic structural information" of muon imaging is coupled with the "high-resolution local response information" of fiber optic sensing, realizing the transformation from "single physical field detection" to "multi-physical field collaborative inversion", which significantly improves the identification accuracy and drilling decision reliability under complex geological conditions.

[0064] Real-time monitoring platform: Displays comprehensive analysis data and system status, provides a real-time monitoring interface, enabling operators to monitor the drilling process and obtain timely feedback; Specifically, data from the integrated drilling real-time monitoring model is displayed in the form of charts such as trend graphs, bar charts, and line charts. This includes relevant parameters such as drilling depth, drilling pressure, rotation speed, temperature, and strain. It can also include formation profiles and images of drilling equipment status, helping operators track drilling progress and equipment status, and providing intuitive visual information to assist in analysis and decision-making.

[0065] Drilling control module: Generates drilling paths and performance optimization strategies based on fused drilling data; As a further embodiment, the drilling control module 1033 is used to make optimization decisions on the drilling path and construction parameters based on the fused data output by the data fusion module 1031.

[0066] Its core logic is as follows: using fused data as environmental perception input, and generating executable control commands through a progressive chain of "state assessment → anomaly triggering → path planning → parameter tuning → closed-loop feedback". The specific implementation process is as follows: Integrating data parsing and feature extraction: The "drilling fusion data" output by the data fusion module includes three core inputs: a three-dimensional fusion field. (Characterizing the comprehensive physical properties of the surrounding rock), probability distribution of anomalies (Characterizing the spatial distribution probability of anomalies) and risk indicators (Characterizing drilling safety).

[0067] The drilling control module first extracts features from the above data, which serve as the basis for subsequent decisions: Density anomaly gradient: Temperature change rate: Strain concentration degree: Comprehensive risk indicators: ,in, These are the weighting coefficients.

[0068] Path optimization triggering conditions and decision logic: Path optimization is not initiated at every drilling stage. Instead, a three-level progressive triggering mechanism is set up based on the real-time identification results of the fused data: Normal drilling status (green): When the following conditions are met and If the surrounding rock conditions ahead are deemed favorable, the control module maintains the current drilling path and parameters, continuously monitors the drilling, and does not trigger path optimization.

[0069] Warning adjustment status (yellow): When the conditions are met but If a potential anomaly is detected ahead but the risk is still within a controllable range, the control module triggers a local fine-tuning strategy, adjusting the drilling speed without changing the main path direction. Drilling pressure and speed Make small adaptive adjustments to smoothly transition through abnormal regions.

[0070] Obstacle avoidance optimization state (red): When the condition is met When a high-risk abnormal geological body (such as a water-rich area, fracture zone, or cavity) is detected ahead, the control module initiates global path replanning, triggering the following complete optimization process.

[0071] Drilling path optimization model construction: In the red obstacle avoidance state, the path planning problem is transformed into a constrained optimization problem, minimizing the drilling cost function while ensuring safety: , in, This indicates the risk value along the path, guiding the path to proactively avoid high-risk areas; It indicates the path curvature, constrains the drill pipe's turning ability, and prevents excessive curvature from causing the drill to get stuck; This indicates the path length, aiming to minimize the drilling distance while satisfying obstacle avoidance requirements. This is a weighting coefficient that can be dynamically adjusted based on construction priorities (safety first / efficiency first).

[0072] During this process, the probability distribution of anomalous bodies As a spatial constraint, the posterior probability The voxels are defined as impassable areas to ensure that replanned paths spatially bypass high-risk anomalies.

[0073] Path adjustment decision rules: Based on the planned path and the characteristics of the fused data in real time, the following refined adjustment rules are executed: ①When At that time, an avoidance strategy is generated, the drilling direction is adjusted, and the deflection angle is positively correlated with the risk gradient: ; ②When When the density decreases significantly, possibly indicating a cavity, a deceleration or drilling stop strategy is triggered, and drilling pressure and rotation speed are reduced simultaneously to prevent the drill bit from spinning idly or collapsing and burying the drill bit.

[0074] ③When When the strain is concentrated, the surrounding rock is determined to be unstable, and reinforcement suggestions or slow-moving strategies are generated to reduce the advance speed and minimize disturbance to the surrounding rock.

[0075] Based on real-time feedback from fused data, drilling parameters are adaptively adjusted, as follows: Adaptive drilling speed: , Drill pressure adaptive:

[0076] Speed ​​adaptive: , in, , , To set the initial parameters, , , The above parameters are adjusted by a small, smooth adjustment under yellow alert conditions, and by a larger, rapid response adjustment under red obstacle avoidance conditions.

[0077] Because the fused data is continuously updated during drilling, path and parameter optimization is not completed all at once. A recursive optimization method is used to modify the path step by step: The recursive step size is: It will be updated in real time. and As input to the state observation, closed-loop control is achieved, enabling the system to maintain stability and response speed even under drilling disturbances and changes in geological conditions.

[0078] The final output of the drilling control module includes: the optimized three-dimensional drilling path trajectory (including coordinate sequence and direction vector); real-time drill bit attitude adjustment commands (azimuth and dip angle changes); drilling parameter adjustment commands (speed, drilling pressure, and rotation speed control values); risk warning information and corresponding control strategies (avoidance / slow progress / stop drilling).

[0079] Through the complete decision-making chain of "state assessment → anomaly triggering → path planning → parameter tuning → closed-loop feedback" described above, the effective transformation of fused data into control commands is achieved. When the fused data shows that the surrounding rock conditions ahead are good ( The system maintains the existing path and proceeds efficiently; when the probability of anomalies increases but the risk is controllable ( but The system performs local parameter fine-tuning to ensure a smooth transition; when a high-risk anomaly is identified ( The system automatically triggers global path replanning and synchronously adjusts drilling parameters to proactively avoid complex geological formations. Within this framework, the system also allows operators to manually intervene in control parameters via a host computer to address special working conditions not covered by the algorithm, balancing intelligence and operational flexibility.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A directional drilling system based on muon navigation and distributed fiber optic sensing, characterized in that, include: The muon navigation and detection module is used to collect energy loss and scattering signals when muon rays pass through the strata above the drill bit, locate the directional drilling bit based on the signals, and generate a surrounding rock density distribution image. The distributed optical fiber sensing module is used to demodulate the backscattered light signal collected by the distributed optical fiber sensor, extract the physical parameters distributed along the optical fiber space, and combine the extracted physical parameters to analyze the changes in the optical signal during drilling and determine the various environmental variables monitored around the drill pipe during drilling. The data fusion and control system is used to match and fuse generated surrounding rock density distribution images with various physical monitoring quantities around the drill pipe during drilling to obtain drilling fusion data, and to generate drilling paths and effect optimization strategies based on the drilling fusion data.

2. The directional drilling system based on muon navigation and distributed fiber optic sensing as described in claim 1, characterized in that, The muon navigation and detection module includes a muon detection array, which comprises a ground reference array and a moving array while drilling. The ground reference array consists of multiple controllable muon detection array units or controllable artificial muon sources, used to capture the incident energy and angle information of muon rays on the ground, or to generate high-energy, high-flux muon beams. The moving array while drilling is used to capture muon ray signals penetrating the upper strata during drilling in real time.

3. The directional drilling system based on muon navigation and distributed fiber optic sensing as described in claim 2, characterized in that, The arrangement of the ground reference array is set according to the geometric layout of the detection scenario. When the detection scenario is a linear project or a structure with axial extension characteristics, a linear layout is adopted. When the detection scene is a planar distribution or a large-scale regional detection, a grid layout is adopted; When the detection scenario involves detecting key local targets or requires high-precision inversion of the central area, a ring layout is adopted.

4. The directional drilling system based on muon navigation and distributed fiber optic sensing as described in claim 2, characterized in that, The spacing between the detector elements in the muon detector array is determined comprehensively based on the muon flux characteristics, target size, and required spatial resolution.

5. The directional drilling system based on muon navigation and distributed optical fiber sensing as described in claim 1, characterized in that, In the muon navigation and detection module, the directional drilling bit is located according to the signal. Specifically, this includes: inverting the muon's crossing path based on the time difference, energy attenuation and scattering information between the muon on the ground reference array and the drilling moving array, and calculating the spatial position and attitude parameters of the drilling moving array or the drill bit accordingly.

6. The directional drilling system based on muon navigation and distributed optical fiber sensing as described in claim 1, characterized in that, In the distributed optical fiber sensing module, the demodulation processing of the backscattered light signal collected by the distributed optical fiber sensor includes: performing coherent detection on the continuously acquired Rayleigh scattering echoes to obtain optical field phase information; extracting the peak frequency shift by scanning or fitting the Brillouin gain spectrum to invert the temperature and strain distribution; and calculating the temperature distribution along the optical fiber based on the relationship between the intensity ratio of Raman anti-Stokes light and Stokes light and temperature.

7. The directional drilling system based on muon navigation and distributed fiber optic sensing as described in claim 1, characterized in that, The matching and fusion of the generated surrounding rock density distribution image and various physical monitoring quantities around the drill pipe during drilling includes: The image data of surrounding rock density distribution and various physical monitoring quantities are standardized and normalized. Among them, the various physical monitoring quantities include temperature, strain and vibration signals. Based on the coupling relationship between surrounding rock density and temperature, strain and vibration, a fusion identification model is constructed. The fusion identification model adopts a weighted fusion model or a probabilistic fusion model. After fusion of the identification model, one or more of the following are obtained: three-dimensional fused physical property field, anomaly probability distribution map, and drilling risk assessment index.

8. The directional drilling system based on muon navigation and distributed optical fiber sensing as described in claim 1, characterized in that, The weighted fusion model is represented as: in, To fuse feature parameters, These are weighting coefficients, which can be determined based on experience or training data. , , and This represents the density distribution, temperature, strain, and vibration signals of the surrounding rock after coordinate unification. The probabilistic fusion model uses the Bayesian method, and is expressed as follows: , in, It indicates abnormal geological bodies (such as water-rich areas, cavities, etc.). For observation data, Represents the posterior probability. Represents the likelihood function. This represents the prior probability.

9. The directional drilling system based on muon navigation and distributed optical fiber sensing as described in claim 1, characterized in that, In data fusion and control systems, drilling paths and performance optimization strategies are generated based on fused drilling data, including: Based on drilling fusion data, density anomaly gradient, temperature change rate, strain concentration degree and comprehensive risk indicators are extracted; A three-tiered progressive triggering mechanism is established based on comprehensive risk indicators and set thresholds. When the conditions are met... and When the surrounding rock conditions ahead are deemed favorable, the control module maintains the current drilling path and parameters, continuously monitors, and does not trigger path optimization. When satisfied but If a potential anomaly is detected ahead but the risk is still within a controllable range, the control module triggers a local fine-tuning strategy, adjusting the drilling speed without changing the main path direction. Drilling pressure and speed Make small adaptive adjustments to smoothly transition through abnormal regions; When satisfied When a high-risk anomalous geological body is detected ahead, the control module initiates global path replanning, triggering an optimization process. This process includes: transforming the path planning problem into a constrained optimization problem; minimizing the drilling cost function while ensuring safety; and adjusting the path based on real-time fused data characteristics, density anomaly gradient, temperature change rate, strain concentration, comprehensive risk indicators, and set path adjustment decisions, based on the already planned path.

10. A directional drilling method based on muon navigation and distributed fiber optic sensing, characterized in that, The process includes the following steps: collecting energy loss and scattering signals of muon rays as they pass through the strata above the drill bit; locating the directional drilling bit based on the signals; and generating a surrounding rock density distribution image. The backscattered light signal collected by the distributed optical fiber sensor is demodulated, and physical parameters distributed along the optical fiber are extracted. The changes in the light signal during the drilling process are analyzed in combination with the extracted physical parameters to determine the various environmental variables monitored around the drill pipe during the drilling process. The generated surrounding rock density distribution image and various physical monitoring quantities around the drill pipe during the drilling process are matched and fused to obtain drilling fusion data. Based on the drilling fusion data, drilling path and effect optimization strategies are generated.

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