Underground coal mine intelligent measurement while drilling and real-time communication system

Abstract

The invention discloses an underground coal mine intelligent measurement while drilling and real-time communication system, which belongs to the technical field of well logging while drilling, and specifically comprises the following steps: dividing a three-dimensional geologic model of a to-be-drilled area into space correlation grid units along a preset drilling trajectory; obtaining rock stratum parameters of any grid unit, and constructing a waveguide network model based on the rock stratum parameters; the initial attenuation gradient of the stress wave energy of all the grid units along the drill rod is calculated, the initial sensor sampling rate corresponding to each grid unit is determined, and data acquisition is carried out; real-time drilling parameters in the drilling process are obtained, rock stratum parameters of a grid unit where a current drill bit is located are updated, the correction attenuation gradient of stress wave energy corresponding to the current grid unit along a drill rod is calculated, the sensor sampling rate corresponding to the grid unit is determined, and data collection is carried out; according to the invention, adaptive optimization of the sampling rate of the sensor in the drilling process is realized, and the data precision and the acquisition efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logging while drilling, and in particular to an intelligent logging while drilling and real-time communication system for underground coal mines. Background Art

[0002] LWD technology in underground coal mines can be used to guide near-horizontal drilling operations and evaluate complex formations. It has broad application prospects in coal mine gas extraction, small geological anomaly detection, and water control projects. LWD uses specialized logging probes to measure the physical parameters of the formations surrounding the borehole while drilling. This data is then transmitted to the outside of the borehole using a real-time data transmission device. This measured data provides a basis for drilling operators to accurately classify the lithology of the drilled formation and adjust the drilling trajectory.

[0003] In the prior art, each measuring probe is powered by a battery pack, and the battery pack and the intrinsically safe protection circuit are encapsulated in an independent battery compartment. Limited by the instrument space and relevant regulations on explosion-proof electrical equipment in coal mines, the size of the probe (length and outer diameter) and the capacity of the battery pack need to be within a certain range. When the logging while drilling system in coal mines is in normal use, the measuring probe is sent into the hole together with the drill bit for measurement while drilling. The drilling trajectory is calculated by accumulating the measured data and the hole position information of each measuring point, while other logging parameters are measured continuously in real time. During the logging while drilling process, all measuring units remain powered and use a fixed sampling frequency for data acquisition;

[0004] In practical applications, if a high sampling rate is fixedly used for data collection, although detailed data can be captured, it will increase power consumption and generate redundant information, especially in uniform rock formations, which may easily lead to waste of storage and transmission resources; if a low sampling rate is fixedly used, although power can be saved, key dynamic features (such as stress wave mutations at rock interfaces) may be missed, resulting in inaccurate identification of geological anomalies. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent measurement-while-drilling and real-time communication system for underground coal mines to solve the following technical problems:

[0006] The high sampling rate of underground logging while drilling systems in coal mines due to the fixed sampling frequency increases energy consumption and data redundancy, while the low sampling rate easily misses key dynamic features, resulting in a contradiction between resource waste and inaccurate identification of geological anomalies.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] Intelligent measurement while drilling and real-time communication system for underground coal mines, including:

[0009] a data acquisition module for acquiring a three-dimensional geological model of the area to be drilled and dividing the three-dimensional geological model into a plurality of spatially correlated grid cells along a preset drilling trajectory, each grid cell corresponding to a trajectory segment on the preset drilling trajectory;

[0010] A model building module is used to obtain the rock formation parameters of any grid cell, wherein the rock formation parameters include elastic modulus, Poisson's ratio and rock formation density, and calculate the propagation velocity, attenuation coefficient and waveform distortion rate of the drill pipe axial stress wave in each grid cell based on the rock formation parameters, thereby constructing a waveguide network model;

[0011] an initial determination module, configured to calculate an initial attenuation gradient of stress wave energy of all grid cells along the drill pipe based on the waveguide network model, determine an initial sensor sampling rate corresponding to each grid cell based on the attenuation gradient, and perform data acquisition on each grid cell based on the initial sensor sampling rate;

[0012] a data analysis module for analyzing drilling parameters in real time during drilling, wherein the drilling parameters include weight on bit, rotational speed, and torque; updating the rock formation parameters of the grid cell where the drill bit is currently located based on the drilling parameters; and modifying the waveguide network model based on the updated rock formation parameters to obtain a modified waveguide network model;

[0013] The result generation module is used to calculate the corrected attenuation gradient of the stress wave energy corresponding to the grid unit where the drill bit is currently located along the drill pipe based on the corrected waveguide network model, determine the sensor sampling rate corresponding to the grid unit based on the corrected attenuation gradient, and perform data acquisition.

[0014] As a further solution of the present invention: in the model construction module, the specific construction process of the waveguide network model is:

[0015] Each grid unit is regarded as an independent node, and all nodes are connected according to the preset drilling trajectory. Directed connection edges are created between adjacent nodes, and each directed connection edge is assigned stress wave conduction velocity, energy attenuation coefficient and waveform distortion parameters to form a unidirectional chain topology structure and obtain the waveguide network model.

[0016] As a further solution of the present invention: In the data analysis module, the specific process of updating the rock formation parameters is as follows:

[0017] The current drilling parameters are converted into rock formation response characteristics through a physical-empirical model. A prior probability distribution is constructed based on the rock formation parameters of each grid cell in the initial three-dimensional geological model. The physical relationship between the real-time drilling parameters and the rock formation parameters is used to establish a likelihood function. The prior distribution and the likelihood function are combined through the Bayesian theorem to obtain the posterior probability distribution of the rock formation parameters of the current grid cell. The mean value of the rock formation parameters corresponding to the posterior distribution is used as the update value to obtain the updated rock formation parameters of the grid cell.

[0018] As a further solution of the present invention: in the data analysis module, the specific process of obtaining the attenuation gradient is:

[0019] The drill rod is divided into the order of the data grid units to generate a drill rod node sequence. A preset pulse signal is injected into the first drill rod node as the initial excitation for stress wave propagation. For any drill rod node, the displacement of the adjacent node in the previous time step is combined with the wave velocity and attenuation coefficient of the grid unit to which the current drill rod node belongs, and the new displacement value corresponding to the current drill rod node is calculated by the finite difference method.

[0020] The above process is repeated until the preset pulse signal propagation completely covers all grid cells. The time-step new displacement value change curve of each drill rod node on the entire time axis is extracted. The displacement change rate corresponding to the drill rod node is calculated based on the time-step new displacement value change curve. The sum of the squares of the displacement change rates is multiplied by the rock density of the grid cell to obtain the total energy value corresponding to the grid cell. The ratio of the total energy difference between adjacent drill rod nodes and the length of the grid cell is calculated to obtain the attenuation gradient corresponding to the grid cell.

[0021] As a further solution of the present invention: In the initial determination module, the specific process of determining the sampling rate corresponding to each grid unit is:

[0022] When the attenuation gradient SI of the stress wave energy corresponding to any grid cell along the drill rod is less than SI1, the sensor sampling rate corresponding to the grid cell is determined to be the highest gear; when the attenuation gradient SI1≤SI<SI2 of the stress wave energy corresponding to any grid cell along the drill rod is less than or equal to SI1, the sensor sampling rate corresponding to the grid cell is determined to be the second gear; when the attenuation gradient GSI of the stress wave energy corresponding to any grid cell along the drill rod is greater than or equal to GSI2, the sensor sampling rate corresponding to the grid cell is determined to be the third gear, wherein SI1 and SI2 are set as preset attenuation gradient thresholds, and SI1<SI2.

[0023] As a further solution of the present invention: in the result generation module, if the difference between the modified attenuation gradient SI1' and the initial attenuation gradient SI1 of any grid unit is greater than or equal to a preset threshold, the sensor sampling rate of the grid unit is directly set to the highest level and data collection is performed.

[0024] As a further solution of the present invention: if the difference is less than a preset difference threshold, the sensor sampling rate corresponding to the grid unit is determined based on the modified attenuation gradient, and data collection is performed.

[0025] As a further solution of the present invention: the data analysis module also includes obtaining the drilling parameters corresponding to adjacent moments for any grid unit, calculating the absolute difference value of each parameter respectively and using it as the three-dimensional coordinate component, and obtaining the difference value by calculating the Euclidean distance of each component in the three-dimensional space. If the difference value is less than a preset difference threshold, the rock formation parameters of the grid unit where the current drill bit is located will not be updated.

[0026] Beneficial effects of the present invention:

[0027] 1) The present invention divides the three-dimensional geological model into spatially correlated grid units through the data acquisition module, and constructs a waveguide network model based on the rock formation parameters (elastic modulus, Poisson's ratio, rock formation density) of each grid unit in the model construction module to calculate the conduction velocity, attenuation coefficient and waveform distortion rate of stress waves in different rock formations. The initial determination module and the result generation module dynamically adjust the sensor sampling rate based on the attenuation gradient: a low sampling rate is used in uniform rock formations with a low stress wave attenuation gradient to reduce power consumption and redundant data; the sampling rate is automatically increased in rock interfaces or abnormal areas with a high attenuation gradient to accurately capture the sudden change characteristics of stress waves. Compared with the fixed sampling rate model in the prior art, the present invention avoids the waste of resources caused by high sampling rates in uniform rock formations, while ensuring that key data in complex rock formations are not missed, and realizes the dynamic optimization configuration of data acquisition efficiency and storage and transmission resources.

[0028] 2) The data analysis module uses physical-empirical models and Bayesian theorem to convert real-time drilling parameters (weight on bit, rotational speed, and torque) into rock formation response characteristics. This module then combines prior probability distributions with likelihood functions to update the rock formation parameters for the current grid cell and correct the waveguide network model. This process dynamically calibrates the initial geological model using measured data during drilling. The difference in drilling parameters between adjacent moments determines whether to trigger the update mechanism, ensuring that the model reflects the actual formation characteristics in real time. The corrected waveguide network model more accurately calculates stress wave attenuation gradients, enabling the system to dynamically identify lithologic change boundaries and potential anomalies.

[0029] 3) The present invention forms a closed-loop feedback system through a two-stage sampling rate control mechanism of the initial determination module and the result generation module, combined with the dynamic modeling process of the data analysis module. On the one hand, the sampling gear is preset based on the attenuation gradient of the initial waveguide network model to provide a basic data acquisition strategy for conventional drilling scenarios; on the other hand, the attenuation gradient changes are monitored in real time during the drilling process, and the sampling rate is forced to increase when the difference between the correction value and the initial value exceeds the preset threshold to ensure the data integrity of sudden geological changes. This "pre-judgment + real-time correction" collaborative mechanism not only reduces the dependence of fixed high sampling rates on hardware endurance, but also adapts to the complex and changeable formation conditions in coal mines through dynamic adjustment, avoiding measurement blind spots or waste of resources due to rigid sampling strategies, and significantly enhancing the robustness of the system in different geological environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present invention will be further described below with reference to the accompanying drawings.

[0031] Figure 1 It is a flow chart of the intelligent measurement while drilling and real-time communication system for underground coal mines of the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] See also Figure 1 As shown, the present invention is an intelligent measurement while drilling and real-time communication system for underground coal mines, comprising:

[0034] The data acquisition module is used to obtain a 3D geological model of the area to be drilled. According to the spatial direction of the preset drilling trajectory, the 3D geological model is divided into several grid cells and numbered sequentially. The grid cells are labeled as Node1, Node2, ..., NodeN from the starting point to the end point along the drilling direction. Each node corresponds to a specific rock layer section on the actual drilling path.

[0035] A model construction module is used to obtain the rock formation parameters of any grid cell, where the rock formation parameters include elastic modulus, Poisson's ratio, and rock formation density. Based on the rock formation parameters, the conduction velocity, attenuation coefficient, and waveform distortion rate of the drill pipe axial stress wave in each grid cell are calculated. According to the forward direction of the drilling trajectory, all nodes are connected in sequence with directed line segments from Node1 to Node2, Node2 to Node3, etc. to form a chain-like structure. The direction of each line segment represents the propagation direction of the stress wave in the drill pipe along the drilling path. The directed connecting edges between adjacent nodes are assigned specific stress wave conduction velocity parameters, energy attenuation coefficient, and waveform distortion parameters through experimental measurement or geological data inversion, thereby constructing a waveguide network model.

[0036] an initial determination module, configured to calculate an initial attenuation gradient of stress wave energy of all grid cells along the drill pipe based on the waveguide network model, determine an initial sensor sampling rate corresponding to each grid cell based on the attenuation gradient, and perform data acquisition on each grid cell based on the initial sensor sampling rate;

[0037] a data analysis module for analyzing drilling parameters in real time during drilling, wherein the drilling parameters include weight on bit, rotational speed, and torque; updating the rock formation parameters of the grid cell where the drill bit is currently located based on the drilling parameters; and modifying the waveguide network model based on the updated rock formation parameters to obtain a modified waveguide network model;

[0038] The result generation module is used to calculate the corrected attenuation gradient of the stress wave energy corresponding to the grid unit where the drill bit is currently located along the drill pipe based on the corrected waveguide network model, determine the sensor sampling rate corresponding to the grid unit based on the corrected attenuation gradient, and perform data acquisition.

[0039] In a preferred embodiment of the present invention, in the model construction module, the specific construction process of the waveguide network model is:

[0040] Each grid unit is regarded as an independent node, and all nodes are connected according to the preset drilling trajectory. Directed connection edges are created between adjacent nodes, and each directed connection edge is assigned stress wave conduction velocity, energy attenuation coefficient and waveform distortion parameters to form a unidirectional chain topology structure and obtain the waveguide network model.

[0041] During operation, each grid cell in the 3D geological model is first numbered sequentially according to the spatial orientation of the pre-determined drilling trajectory. For example, along the drilling direction, the grid cells are labeled Node1, Node2, and so on, from the starting point to the end point. Each node corresponds to a specific rock formation segment along the actual drilling path. Subsequently, all nodes are connected in sequence using directed line segments, from Node1 to Node2, Node2 to Node3, and so on, along the forward direction of the drilling trajectory, forming a chain-like structure. The direction of each line segment represents the direction of stress wave propagation in the drill pipe along the drilling path, for example, from the drill bit toward the orifice. For the directed edges connecting adjacent nodes, specific stress wave propagation velocity parameters, energy attenuation coefficients, and waveform distortion parameters are assigned to each edge through experimental measurements or geological data inversion. The rock formation parameters (elastic modulus, Poisson's ratio, and rock density) of each grid cell are calculated using theoretical formulas. Ultimately, a unidirectional chain waveguide network model is constructed, which incorporates node connection relationships and parameter attributes.

[0042] By setting nodes and directed edges, the propagation path and physical characteristics of stress waves under the interaction between drill pipe and rock formations can be clearly simulated. By assigning specific parameters to the connecting edges, the influence of different rock formations on the conduction and attenuation of stress waves can be quantified. Based on this model, the attenuation gradient of stress wave energy along the drill pipe can be calculated, and the sensor sampling rate can be determined.

[0043] In another preferred embodiment of the present invention, in the data analysis module, the specific process of updating the rock formation parameters is as follows:

[0044] The current drilling parameters are converted into rock formation response characteristics through a physical-empirical model. A prior probability distribution is constructed based on the rock formation parameters of each grid cell in the initial three-dimensional geological model. The physical relationship between the real-time drilling parameters and the rock formation parameters is used to establish a likelihood function. The prior distribution and the likelihood function are combined through the Bayesian theorem to obtain the posterior probability distribution of the rock formation parameters of the current grid cell. The mean value of the rock formation parameters corresponding to the posterior distribution is used as the update value to obtain the updated rock formation parameters of the grid cell.

[0045] First, for the real-time drilling parameters (such as drilling pressure, rotational speed, and torque), these mechanical parameters are converted into rock formation response characteristics using a pre-established physical-empirical model (such as a model that relates drilling pressure to rock formation compressive strength based on Coulomb-Mohr strength theory in rock mechanics). For example, the drilling pressure value is substituted into the model to calculate the corresponding rock formation hardness index. Next, based on the existing rock formation parameters (such as elastic modulus E, Poisson's ratio μ, and rock formation density ρ) for each grid cell in the initial 3D geological model, a prior probability distribution is constructed. For example, the elastic modulus E of a grid cell is assumed to follow a normal distribution with a mean of the initial model value and a standard deviation of a preset value. Then, the physical relationship between the real-time drilling parameters and rock formation parameters (such as the positive correlation between torque and rock formation shear modulus) is used to establish a likelihood function. For example, the likelihood function is set as the conditional probability density function of the current drilling parameters observed under given rock formation parameters. Using Bayes' theorem, the prior probability distribution is multiplied by the likelihood function and normalized to obtain the posterior probability distribution of the rock parameters for the current grid cell. For example, after calculation, the mean of the posterior distribution of the elastic modulus E shifts compared to the prior distribution. Finally, the mean of the rock parameters corresponding to the posterior probability distribution (such as the mean of E in the posterior distribution) is taken as the updated value and replaced with the corresponding parameter in the initial model, completing the update of the rock parameters for the grid cell.

[0046] Using measured data acquired during real-time drilling, the formation parameters in the initial geological model are dynamically calibrated. A physical-empirical model is used to convert drilling parameters into characteristic rock response quantities, establishing a mapping bridge between the measured data and the formation parameters. A prior probability distribution is constructed to integrate the prior knowledge of the initial geological model, while a likelihood function reflects the constraints imposed by the real-time observations on the formation parameters. The Bayesian-based update process seamlessly integrates prior information with real-time observations, enabling continuous correction of model deviations from the actual formation description as drilling progresses. This is particularly true when crossing zones of lithologic variation or encountering unforeseen geological anomalies. Dynamic adjustments to the posterior probability distribution allow the model parameters to more closely align with the actual formation characteristics. This provides more accurate input parameters for subsequent waveguide network model revisions, improving the accuracy of stress wave attenuation gradient calculations. The system can dynamically adjust the sensor sampling rate and data acquisition strategy based on the updated model, enhancing real-time perception of lithologic variations and stress wave propagation patterns in complex formations, ultimately achieving a precise match between measurement-while-drilling data and actual geological conditions.

[0047] In another preferred embodiment of the present invention, in the data analysis module, the specific process of obtaining the attenuation gradient is:

[0048] The drill rod is divided into the order of the data grid units to generate a drill rod node sequence. A preset pulse signal is injected into the first drill rod node as the initial excitation for stress wave propagation. For any drill rod node, the displacement of the adjacent node in the previous time step is combined with the wave velocity and attenuation coefficient of the grid unit to which the current drill rod node belongs, and the new displacement value corresponding to the current drill rod node is calculated by the finite difference method.

[0049] The above process is repeated until the preset pulse signal propagation completely covers all grid cells. The time-step new displacement value change curve of each drill rod node on the entire time axis is extracted. The displacement change rate corresponding to the drill rod node is calculated based on the time-step new displacement value change curve. The sum of the squares of the displacement change rates is multiplied by the rock density of the grid cell to obtain the total energy value corresponding to the grid cell. The ratio of the total energy difference between adjacent drill rod nodes and the length of the grid cell is calculated to obtain the attenuation gradient corresponding to the grid cell.

[0050] First, the drill pipe is divided into nodes corresponding to each grid cell according to the order of the data grid cells along the pre-set drilling trajectory. For example, the first grid cell corresponds to the drill pipe front node P1, the second grid cell corresponds to node P2, and so on, forming a node sequence P1-P2-…-Pn. A predetermined pulse signal (e.g., a sinusoidal signal with amplitude A) is then injected into the first node P1 to simulate the initial excitation of stress waves generated by the drill bit impacting the rock formation during drilling. For any node Pi, the displacement at time t is calculated based on the displacement values of adjacent nodes Pi-1 and Pi+1 in the previous time step (t-Δt) (e.g., if the displacement of Pi-1 at time t-Δt is uprev), combined with the wave velocity v and attenuation coefficient α of the grid cell to which the current node belongs. Using an explicit finite difference method (e.g., the central difference method), a recursive formula is established: ucurrent = (uprev*v*Δt-α*Δt*uprev) + boundary condition terms. This calculates the new displacement of Pi at time t. This recursive process is repeated until the pulse signal propagates from P1 to Pn, covering all grid cells. The displacement change curve for each node throughout the entire time series is then extracted (for example, the displacement-time curve for node P2 shows a trend of first increasing and then decaying). The curve is differentiated to obtain the displacement change rate (for example, the slope of the curve at a certain moment is k). The squares of the displacement change rates at each moment are summed and multiplied by the rock density ρ of the grid cell to obtain the total energy value E = Σ(k²) × ρ. Finally, the ratio of the total energy difference between adjacent nodes to the corresponding grid cell length L is calculated to quantify the degree of stress wave energy attenuation within the cell.

[0051] Numerical simulations are used to quantify the propagation characteristics of stress waves in the coupled drill pipe and rock formation system, providing key parameters for dynamically adjusting data acquisition strategies. Dividing the drill pipe into a sequence of nodes corresponding to grid cells aligns the stress wave propagation process with the actual geological structure, facilitating analysis of energy attenuation patterns in conjunction with rock formation parameters (wave velocity, attenuation coefficient). Finite-difference method-based recursive calculation of node displacements simulates the propagation, reflection, and attenuation of stress waves at different rock formation interfaces (e.g., sudden changes in the displacement curve at interfaces with abrupt lithologic changes). Calculating the total energy value by combining the displacement change rate with rock formation density converts the mechanical vibration signal into a quantitative energy indicator, intuitively reflecting the stress wave attenuation capacity of each unit. Calculating the attenuation gradient essentially establishes a mapping between rock formation characteristics, stress wave energy attenuation, and sampling rate adjustment. Regions with high attenuation gradients (e.g., fault zones) indicate rapid stress wave energy attenuation, necessitating a higher sampling rate to capture transient changes. Regions with low attenuation gradients (e.g., uniform sandstone) can be sampled at a lower rate to conserve resources.

[0052] In another preferred embodiment of the present invention, in the initial determination module, the specific process of determining the sampling rate corresponding to each grid unit is:

[0053] When the attenuation gradient SI of the stress wave energy corresponding to any grid cell along the drill rod is less than SI1, the sensor sampling rate corresponding to the grid cell is determined to be the highest gear; when the attenuation gradient SI1≤SI<SI2 of the stress wave energy corresponding to any grid cell along the drill rod is less than or equal to SI1, the sensor sampling rate corresponding to the grid cell is determined to be the second gear; when the attenuation gradient GSI of the stress wave energy corresponding to any grid cell along the drill rod is greater than or equal to GSI2, the sensor sampling rate corresponding to the grid cell is determined to be the third gear, wherein SI1 and SI2 are set as preset attenuation gradient thresholds, and SI1<SI2.

[0054] First, calculate the stress wave energy attenuation gradient SI value corresponding to each grid cell according to the waveguide network model. Then compare the SI value with the preset double thresholds SI1 and SI2 (for example, set SI1 = 1.0, SI2 = 3.0, and SI1 < SI2): If the SI value of a certain grid cell is less than SI1 (such as SI = 0.8 < 1.0), it is determined that the stress wave energy attenuation in this area is extremely fast (such as corresponding to a lithologic interface or fracture zone), and the sensor sampling rate needs to be set to the highest gear (such as sampling 1000 times per second) to capture the stress wave mutation details at a high frequency; If the SI value is between SI1 and SI2 (such as SI = 2.5, satisfying 1.0 ≤ 2.5 < 3.0), it is determined as a medium attenuation area (such as a conventional layered rock formation), and the second gear sampling rate is adopted (such as sampling 500 times per second) to balance data accuracy and resource consumption; If the SI value is greater than or equal to SI2 (such as SI = 3.5 ≥ 3.0), it is determined as a low attenuation homogeneous rock formation (such as intact limestone), and the third gear lowest sampling rate is adopted (such as sampling 100 times per second) to reduce the acquisition of invalid data. Through this grading mechanism, the sampling rate is directly related to the stress wave attenuation characteristics of the rock formation.

[0055] By the dynamic mapping of the attenuation gradient and the sampling rate, the problems of wasting resources in homogeneous rock formations and missing key data in complex rock formations in the existing fixed sampling mode are solved. Dividing the attenuation gradient into three gears and matching different sampling rates essentially establishes an adaptive adjustment mechanism of "rock formation physical characteristics - signal propagation characteristics - acquisition strategy": Increasing the sampling rate in the high attenuation gradient area (SI < SI1) can ensure that the transient changes of the stress wave at the lithologic interface or geological anomaly are completely captured (such as waveform distortion, energy sudden drop), avoiding the loss of abnormal signals due to too large sampling intervals; Adopting the middle gear in the medium attenuation area (SI1 ≤ SI < SI2) can maintain data continuity during the gradual change of rock formation characteristics, while reducing the power consumption of high-frequency sampling; Adopting a low sampling rate in the low attenuation area (SI ≥ SI2) can greatly reduce the working load of the sensor, extend the battery life and reduce the data storage and transmission pressure. This grading strategy enables the system to dynamically switch the acquisition mode according to the real-time geological conditions, ensuring the measurement accuracy in complex geological scenarios and improving the resource utilization efficiency in conventional scenarios, and ultimately realizing the stable operation of the measurement-while-drilling system in different rock formation environments, providing technical support for accurate geological steering and drilling safety.

[0056] In another preferred embodiment of the present invention, in the result generation module, if the difference between the corrected attenuation gradient SI1' and the initial attenuation gradient SI1 of any grid cell is greater than or equal to the preset threshold, directly set the sensor sampling rate of this grid cell to the highest gear and perform data acquisition.

[0057] By monitoring the difference between the corrected attenuation gradient and the initial value, sudden changes in rock formation properties can be identified promptly. When the difference exceeds a preset threshold, it indicates that the initial model's description of the current formation has significantly deviated (e.g., the actual formation is more fragmented than expected or the lithology is more different). In this case, the stress wave propagation characteristics may undergo nonlinear changes (e.g., increased energy attenuation or a sudden increase in waveform distortion). By directly setting the sampling rate to the highest setting, data acquisition density is increased immediately when rock formation conditions suddenly change, ensuring that stress wave signals (such as high-frequency oscillations and energy drops) at the sudden interface are fully recorded, avoiding abnormal signal loss or interpretation distortion caused by sampling rate lag. This mechanism, serving as the "emergency response" link of the dynamic sampling strategy, compensates for the limitations of the initial model's predictions, enhances the system's sensitivity and adaptability to sudden geological conditions, and enables measurement while drilling to track formation changes in real time. This provides critical data support for drillers to promptly adjust drilling parameters and avoid geological risks (such as accidentally penetrating aquifers or structural zones), ultimately ensuring the safety of drilling projects and the reliability of geological interpretations.

[0058] In another preferred embodiment of the present invention, if the difference is less than a preset difference threshold, the sensor sampling rate corresponding to the grid unit is determined based on the modified attenuation gradient, and data acquisition is performed.

[0059] When there is no significant mutation in the rock formation characteristics, the sampling strategy is maintained in a stable and rational manner to avoid frequent adjustments to the sampling rate due to minor fluctuations. When the difference between the corrected attenuation gradient and the initial value is less than the preset threshold, it indicates that the initial model's prediction is still highly reliable, and the current formation conditions are within the expected range of variation of the model (such as fluctuations in physical property parameters within the same lithologic layer). At this time, the three-speed sampling rules are re-matched based on the corrected attenuation gradient, which can not only use real-time data to fine-tune the model, but also avoid waste of resources caused by overreaction (such as meaningless high-frequency sampling). For example, in a uniform sandstone formation, if the corrected attenuation gradient fluctuates slightly only due to a slight change in rock formation density, but does not exceed the medium attenuation range, maintaining the second-speed sampling rate can ensure data continuity while avoiding the increase in sensor power consumption due to frequent gear switching. This mechanism achieves a smooth response of the sampling strategy to the gradual change process of the formation through the dual logic of "difference threshold judgment + gradient classification matching", which not only ensures the real-time and accuracy of data acquisition, but also optimizes the system operation efficiency, enabling the measurement while drilling system to maintain efficient operation in a stable formation environment. At the same time, it provides a reliable data basis for the continuous calibration of the geological model, ultimately facilitating the refined implementation of drilling projects and the dynamic evaluation of geological conditions.

[0060] In another preferred embodiment of the present invention, the data analysis module also includes obtaining the drilling parameters corresponding to adjacent moments for any grid unit, calculating the absolute difference value of each parameter and using it as the three-dimensional coordinate component, and obtaining the difference value by calculating the Euclidean distance of each component in the three-dimensional space. If the difference value is less than a preset difference threshold, the rock formation parameters of the grid unit where the current drill bit is located will not be updated.

[0061] For any grid cell, first obtain the drilling parameters of two adjacent moments (such as moment t and moment t+Δt) in chronological order, including drilling pressure, rotation speed, and torque. For example, at moment t, the drilling pressure is 10kN, the rotation speed is 150r / min, and the torque is 200N·m; at moment t+Δt, the drilling pressure is 11kN, the rotation speed is 145r / min, and the torque is 210N·m. Then calculate the absolute difference value of each parameter separately: the drilling pressure difference is |11-10|=1kN, the rotation speed difference is |145-150|=5r / min, and the torque difference is |210-200|=10N·m. These three absolute difference values are used as the x, y, and z components of the three-dimensional coordinate system (for example, the x-axis represents the drilling pressure difference, the y-axis represents the rotation speed difference, and the z-axis represents the torque difference), and the difference value is calculated using the Euclidean distance formula;

[0062] During the real-time drilling process in the unit area, the stability of the rock formation parameters is judged by the dynamic fluctuation characteristics of the drilling parameters, and intelligent filtering of the model update is realized. Since the drilling process within the same grid cell is continuous, the rock formation parameters should theoretically remain relatively stable. Small differences in drilling parameters may be due to equipment vibration or operating errors, rather than real rock property changes. By calculating the Euclidean distance of the three-dimensional parameter differences and comparing it with the threshold, it is possible to effectively distinguish between "noise fluctuations" and "rock property mutations": when the difference value is less than the threshold, it means that the drill bit is still in the same rock type or a rock formation with uniform physical properties. Continuing to use the original parameters can not only ensure the stability of the waveguide network model, but also reduce the computational overhead of the Bayesian update (such as avoiding invalid posterior probability iterations). For example, when drilling continuously in a single sandstone formation, the parameter difference is usually maintained at a low level. The system does not update the parameters to avoid response delays caused by high-frequency invalid calculations in the model. This mechanism establishes an "anti-shake" mechanism for updating rock parameters in real-time drilling scenarios, which not only ensures the system's rapid response to sudden changes in rock properties, but also improves the model's computational efficiency in stable formations. Ultimately, it enables the measurement while drilling system to accurately track and efficiently model rock characteristics during dynamic drilling, providing a reliable basis for real-time decision-making in drilling projects.

[0063] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. Intelligent measurement while drilling and real-time communication system for coal mines, characterized by: include: a data acquisition module for acquiring a three-dimensional geological model of the area to be drilled and dividing the three-dimensional geological model into a plurality of spatially correlated grid cells along a preset drilling trajectory, each grid cell corresponding to a trajectory segment on the preset drilling trajectory; A model building module is used to obtain the rock formation parameters of any grid cell, wherein the rock formation parameters include elastic modulus, Poisson's ratio and rock formation density, and calculate the propagation velocity, attenuation coefficient and waveform distortion rate of the drill pipe axial stress wave in each grid cell based on the rock formation parameters, thereby constructing a waveguide network model; An initial determination module is configured to calculate the initial attenuation gradient of the stress wave energy of all grid cells along the drill pipe based on the waveguide network model, determine the initial sensor sampling rate corresponding to each grid cell based on the attenuation gradient, and perform data acquisition; a data analysis module for analyzing drilling parameters in real time during drilling, wherein the drilling parameters include weight on bit, rotational speed, and torque; updating the rock formation parameters of the grid cell where the drill bit is currently located based on the drilling parameters; and modifying the waveguide network model based on the updated rock formation parameters to obtain a modified waveguide network model; The result generation module is used to calculate the corrected attenuation gradient of the stress wave energy corresponding to the grid unit where the drill bit is currently located along the drill pipe based on the corrected waveguide network model, determine the sensor sampling rate corresponding to the grid unit based on the corrected attenuation gradient, and perform data acquisition.

2. The intelligent measurement while drilling and real-time communication system for underground coal mines according to claim 1, characterized in that: In the model construction module, the specific construction process of the waveguide network model is as follows: Each grid unit is regarded as an independent node, and all nodes are connected according to the preset drilling trajectory. Directed connection edges are created between adjacent nodes, and each directed connection edge is assigned stress wave conduction velocity, energy attenuation coefficient and waveform distortion parameters to form a unidirectional chain topology structure and obtain the waveguide network model.

3. The intelligent measurement while drilling and real-time communication system for underground coal mines according to claim 1, characterized in that: In the data analysis module, the specific process of updating the rock formation parameters is as follows: The current drilling parameters are converted into rock formation response characteristics through a physical-empirical model. A prior probability distribution is constructed based on the rock formation parameters of each grid cell in the initial three-dimensional geological model. The physical relationship between the real-time drilling parameters and the rock formation parameters is used to establish a likelihood function. The prior distribution and the likelihood function are combined through the Bayesian theorem to obtain the posterior probability distribution of the rock formation parameters of the current grid cell. The mean value of the rock formation parameters corresponding to the posterior distribution is used as the update value to obtain the updated rock formation parameters of the grid cell.

4. The intelligent measurement while drilling and real-time communication system for underground coal mines according to claim 2, characterized in that: In the data analysis module, the specific process of obtaining the attenuation gradient is as follows: The drill rod is divided into the order of the data grid units to generate a drill rod node sequence. A preset pulse signal is injected into the first drill rod node as the initial excitation for stress wave propagation. For any drill rod node, the displacement of the adjacent node in the previous time step is combined with the wave velocity and attenuation coefficient of the grid unit to which the current drill rod node belongs, and the new displacement value corresponding to the current drill rod node is calculated by the finite difference method. The above process is repeated until the preset pulse signal propagation completely covers all grid cells. The time-step new displacement value change curve of each drill rod node on the entire time axis is extracted. The displacement change rate corresponding to the drill rod node is calculated based on the time-step new displacement value change curve. The sum of the squares of the displacement change rates is multiplied by the rock density of the grid cell to obtain the total energy value corresponding to the grid cell. The ratio of the total energy difference between adjacent drill rod nodes and the length of the grid cell is calculated to obtain the attenuation gradient corresponding to the grid cell.

5. The intelligent measurement while drilling and real-time communication system for underground coal mines according to claim 1, characterized in that: In the initial determination module, the specific process of determining the sampling rate corresponding to each grid unit is as follows: When the attenuation gradient SI of the stress wave energy corresponding to any grid cell along the drill rod is less than SI1, the sensor sampling rate corresponding to the grid cell is determined to be the highest gear; when the attenuation gradient SI1≤SI<SI2 of the stress wave energy corresponding to any grid cell along the drill rod is less than or equal to SI1, the sensor sampling rate corresponding to the grid cell is determined to be the second gear; when the attenuation gradient GSI of the stress wave energy corresponding to any grid cell along the drill rod is greater than or equal to GSI2, the sensor sampling rate corresponding to the grid cell is determined to be the third gear, wherein SI1 and SI2 are set as preset attenuation gradient thresholds, and SI1<SI2.

6. The intelligent measurement while drilling and real-time communication system for underground coal mines according to claim 5, characterized in that: In the result generation module, if the difference between the modified attenuation gradient SI1' and the initial attenuation gradient SI1 of any grid unit is greater than or equal to a preset threshold, the sensor sampling rate of the grid unit is directly set to the highest level and data collection is performed.

7. The intelligent measurement while drilling and real-time communication system for underground coal mines according to claim 6, characterized in that: The method further includes determining a sensor sampling rate corresponding to the grid unit based on the modified attenuation gradient and performing data collection if the difference is less than a preset difference threshold.

8. The intelligent measurement while drilling and real-time communication system for underground coal mines according to claim 1, characterized in that: The data analysis module also includes obtaining the drilling parameters corresponding to adjacent moments for any grid cell, calculating the absolute difference value of each parameter and using it as a three-dimensional coordinate component, and obtaining the difference value by calculating the Euclidean distance of each component in the three-dimensional space. If the difference value is less than a preset difference threshold, the rock formation parameters of the grid cell where the current drill bit is located will not be updated.

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