8208results about "Survey" patented technology

In one aspect of the present invention, a drill string has a drill bit with a body intermediate a shank and a working face. The working face has a plurality of blades converging at a center of the working surface and diverging towards a gauge of the working face. At least one blade has a cutting element with a carbide substrate bonded to a diamond working end with a pointed geometry. The diamond working end also has a central axis which intersects an apex of the pointed geometry. The axis is oriented between a 25 and 85 degree positive rake angle.

The invention relates to an array electric imaging logging based reservoir oil-water identification method and system. Aiming at the resistivity distribution characteristics of mud-invaded formations, a 'five-parameter formation model' is established, which is adapted to the requirement for simulating the resistivity distribution characteristics of different mud types and different invaded formations, and is pioneering in the aspects of forward-inversion models and processing methods of array electric imaging logging; and based on a 'five-parameter inversion' method for array electric imaging logging, an operation of carrying out resistivity distribution profile reconstruction on mud-invaded formations is performed, which reflects the actual differences between different resistivity distribution characteristics or change rules of mud-invaded oil formations or water formations. The method has stronger practicability, and is pioneering in the actual application aspects of qualitative identification and quantitative calculation on reservoir fluid properties.

The invention discloses a multi-source collaborative advanced geological exploration method based on TBM construction, and belongs to the technical field of tunnel engineering geological exploration. A three-dimensional geological model is constructed through multi-scale collaborative operation of directional long exploration drilling, branch short exploration drilling and in-hole transient electromagnetic detection; the directional long exploration adopts a secondary drilling structure and a screw motor directional drilling technology to realize accurate control of a deep stratum, the branch short exploration realizes dynamic and rapid verification through a TBM integrated drilling machine, meanwhile, in-hole transient electromagnetic exploration adopts three-component measurement and an intelligent inversion algorithm to fill the blank of geological information between drill holes, and by means of mutual data correction of the three methods, the detection accuracy is improved. A three-in-one detection system of drilling, geophysical prospecting and intelligent analysis is formed, and then accurate advanced prediction is achieved, so that the problems that a traditional single detection technology is low in precision and insufficient in coverage range are effectively solved, the water inrush risk is remarkably reduced, construction safety is guaranteed, and the method is effectively suitable for deep tunnel TBM safety construction.

The invention relates to the technical field of geothermal drilling monitoring management, in particular to a construction monitoring system and method for geothermal drilling. The method comprises the following steps: constructing a state variable set, quantifying structural disturbance intensity by adopting a disturbance derivative method, normalizing the structural disturbance intensity, and defining a state transition tensor mapping behavior influence coefficient; in combination with the high-order derivative of the disturbance trend function and the behavior collaborative change matrix, an offset risk factor is generated through fusion for early warning; dividing a well drilling area into three-dimensional grids, calculating local stress potential energy, adjusting weight through a dynamic sensitive belt, and generating a risk distribution diagram through smooth filtering; performing closed-loop dynamic regulation and control by using risk potential energy centroid coordinates and gradient field inversion intervention vectors for medium and high risk areas; and through multi-period risk potential energy tensor analysis, calculating the matching degree of a self-healing index and a stable cone region, evaluating the self-healing capability of the system and feeding back a regulation and control strategy. According to the invention, a closed-loop monitoring and active control system for nonlinear risks of geothermal drilling is formed.

The invention provides a high-negative-pressure gas extraction drill hole full-life-cycle efficiency evaluation method, and belongs to the technical field of mineral exploitation. A ground stress distribution model is established through microseism monitoring to determine drill hole arrangement parameters; a distributed optical fiber sensing system and a multi-parameter gas flow meter are used for monitoring the stress change around a drill hole and gas extraction data in real time, the coal fracture development degree is obtained in combination with sound wave testing, and all the data are input into a physical numerical value coupling model to calculate a permeability dynamic evolution curve. Establishing a permeability influence coefficient matrix and determining a key parameter weight, constructing an evaluation index system including the extraction amount, the permeability change rate, the drilling life and the coverage range, and performing multi-objective optimization configuration by applying a Pareto optimal solution theory. The technical problem that full-life-cycle dynamic monitoring and comprehensive efficiency evaluation of the high-negative-pressure gas extraction drill hole are lacked at present is solved.

The invention relates to the technical field of petroleum engineering intelligent drilling data processing, in particular to a multi-source data fusion while-drilling formation pressure monitoring system and method, and the system comprises a data collection verification module, a data transmission alignment module, a pressure prediction engine module, a closed-loop optimization module and an intelligent regulation and control module. The data acquisition and verification module acquires pore pressure, annulus equivalent circulation density and vibration spectrum data through the circumferential sensor array, the closed-loop optimization module updates model parameters based on measured data after drilling and triggers periodic reconstruction, and the geological rule constraint unit verifies a gradient change threshold. The intelligent regulation and control module generates a slurry density adjustment instruction and rechecks and issues the slurry density adjustment instruction through the accident case knowledge base; and the three-dimensional dynamic rendering unit realizes pressure gradient visualization. The problems of prediction hysteresis, model mismatch and frequent manual intervention in formation pressure monitoring while drilling are solved through multi-source data real-time fusion, model dynamic optimization and autonomous decision closed loop.

The invention provides a multi-sensor dynamic calibration method in rotary geosteering drilling, and relates to the technical field of petroleum engineering and drilling, and the method comprises the following steps: collecting original sensing data in real time through a multi-source sensor array deployed on a rotary geosteering drilling tool, wavelet packet decomposition is combined with an improved sliding window algorithm to carry out online denoising processing on original data, and a three-dimensional feature matrix containing environmental interference factors is established; and based on the three-dimensional feature matrix, constructing a dynamic calibration model based on depth time sequence association, respectively processing sensor body feature flow and environment interference feature flow by using a dual-channel LSTM network, performing dynamic weight distribution and fusion on dual-channel features by means of a gating attention mechanism, and outputting a dynamic deviation compensation coefficient of each sensor. The parameter set is generated and reconstructed through the three-dimensional feature matrix, the two-channel network and cooperative calibration, the drilling efficiency and precision are improved, errors are reduced, and intelligent development is promoted.

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a well-seismic integrated horizontal well geosteering risk evaluation method, which comprises the following steps: S1, acquiring three-dimensional seismic data, measurement while drilling data and geological logging data through a well-seismic integrated data acquisition platform; s2, constructing a horizontal well geosteering dynamic model based on multi-source heterogeneous data, and integrating a seismic inversion result and real-time drilling parameters; by establishing a well-seismic data dynamic fusion mechanism and a multi-source risk quantification model, the problem of space-time dislocation of seismic inversion and measurement-while-drilling data is solved, the recognition precision of a fault boundary, a lithologic interface and a pressure abnormal zone is ensured to be matched with drilling position changes in real time, risk misjudgment caused by model updating lag in a traditional method is eliminated, and the accuracy of the method is improved. The accuracy and timeliness of geosteering decision making of the complex structure area are improved; by monitoring well track deviation and geological model prediction deviation in real time, the risks of well wall instability and target deviation are avoided, and the operation safety of the horizontal well in the full life cycle is guaranteed.

The invention relates to an intelligent inversion method and system for well drilling overflow shaft gas distribution based on a self-encoder, and belongs to the technical field of oil gas and geothermal development drilling and completion engineering. Comprising the following steps: step 1, accurately solving multiphase flow parameters of a shaft; 2, in combination with the drilling working condition and the geological condition of a specific overflow high-risk well section, based on a Monte Carlo sampling method, eight parameters are changed, uniform sampling is carried out, and a high-precision simulation data set is formed; step 3, constructing a neural network model for gas cut state inversion based on an auto-encoder neural network; 4, training is carried out; 5, standardizing one-dimensional time sequence parameters in the monitoring data; and inputting into a trained neural network model for gas cut state inversion based on an auto-encoder neural network to obtain distribution data of the overflow gas in the shaft in a time period in which the time sequence parameter at the current moment is located. According to the invention, the real-time rapid inversion of the gas distribution in the parallel cylinder during the drilling overflow parallel control period is realized.

The invention discloses a shale oil reservoir geology-engineering double-dessert compressibility comprehensive evaluation method, and relates to the technical field of petroleum and natural gas yield increase transformation and geotechnical engineering, in particular to a shale oil reservoir geology-engineering double-dessert compressibility comprehensive evaluation method, which comprises the following steps of performing normalization processing on a logging basic parameter standard deviation; geological dessert parameters are established by calculating the weight of each factor; influence factors influencing the longitudinal expansion capacity of the hydraulic fracture of the target reservoir are obtained; constructing a geological-engineering double-dessert compressibility comprehensive evaluation model of three types of lithofacies'reservoir performance-transformation potential 'synergistic mechanisms; and establishing a geological-engineering double-dessert compressibility kernel density estimation map, and dividing the reservoir compressibility on the premise of adaptive bandwidth. According to the method, the operability and prediction precision of an evaluation result are remarkably improved, and an innovative decision support tool is provided for horizon optimization and scheme design of shale oil reservoir fracturing transformation.

The invention relates to the technical field of petroleum and natural gas engineering, and discloses an oil and gas reservoir optimized mining method based on multi-modal data, which comprises the following steps: constructing an oil and gas reservoir multi-modal data acquisition system, seismic wave field data, logging interpretation data, production dynamic data, micro-seismic monitoring data, underground temperature and pressure time sequence data and shaft structure parameter data are obtained through the acquisition system; and performing space-time alignment processing on the acquired multi-modal data, establishing a unified geological coordinate system and a time reference, and eliminating data isomerism caused by different acquisition frequencies and spatial resolutions. A six-dimensional heterogeneous data acquisition system covering a seismic wave field, well logging interpretation, production dynamics, micro-seismic monitoring, an underground temperature and pressure time sequence and shaft structure parameters is constructed, so that the depiction precision of a reservoir porosity field, a permeability field, a saturation field and a pressure field is essentially improved.

The grouting drilling orifice reinforcement construction method based on intelligent control comprises the five steps that S1, drilling machine parameter signals are collected through operation of a drilling machine, and meanwhile geological data signals are collected through in-hole detection equipment; s2, the drilling machine parameter signals and the geological data signals are input into an intelligent decision-making center for fusion processing, and geological sensing signals are generated; s3, based on the geological sensing signal, calling a preset decision-making model through the intelligent decision-making center for calculation, and generating a grouting control instruction signal; s4, grouting operation is executed according to the grouting control instruction signal, and meanwhile a grouting process monitoring signal is collected and fed back to the intelligent decision center in real time; and S5, after grouting is completed, hole opening stable monitoring signals are continuously collected, and when the effect evaluation signals do not reach the standard, reinforcing grouting signals are automatically triggered, and grouting operation is executed again. According to the intelligent die adjusting method for the die-casting machine, the problems that drill holes are prone to collapse, reinforcement depends on experience, and quality is difficult to control can be solved.

The invention discloses a wireless layered water injection communication method and device, electronic equipment and a storage medium. The method comprises the steps that water absorption characteristic curve information of stratums corresponding to all water distributors in a target well is obtained, a target stratum is determined according to the water absorption characteristic curve information, an instruction for obtaining characteristic parameters of a reference stratum is received, and a sound wave signal generator corresponding to the reference stratum is controlled to emit a sound wave signal; the sound wave signal is transmitted to a target water distributor corresponding to the target stratum, so that the target water distributor sends a pressure pulse signal to a wellhead controller of the target well, characteristic parameters of the reference stratum are obtained, and the pressure pulse signal is a signal used for indicating the characteristic parameters of the reference stratum. After the target stratum is determined, the sound wave signal is transmitted to the target water distributor corresponding to the target stratum through the sound wave signal generator, then the target water distributor sends the underground data to the wellhead controller through the pressure pulse signal, and the problems that the whole well communication efficiency is low and the communication reliability of partial layers is poor are solved.

A while-drilling multi-element signal sensing and early warning method for coal rock dynamic disasters comprises the steps that in the early stage of coal seam recovery, bedding gas extraction drilling is utilized, while-drilling signals generated in the drilling period of each drill rod are collected in real time through a multi-parameter sensing system integrated with a drilling machine, and while-drilling data are obtained based on the while-drilling signals; constructing a mechanical parameter inversion model, and calculating the compressive strength of the coal rock mass; a gas parameter inversion model is constructed, and gas pressure and coal rock mass permeability are obtained through decoupling; according to the method, coal rock mass compressive strength, gas pressure and coal rock mass permeability are used as core indexes, a spatial interpolation method is adopted to realize continuous representation of disaster risk distribution of a whole area, and a comprehensive risk index is established accordingly; and according to the value range of the comprehensive risk index, further quantifying the risk level of the coal and rock mass dynamic disaster based on the comprehensive risk index. According to the method, real-time sensing, accurate inversion and active prevention and control of coal rock dynamic disasters can be realized, and the problems of low detection accuracy, poor real-time performance, insufficient overall performance and the like in the traditional technology can be effectively solved.

Systems and methods of the present disclosure includes a mixer configured to mix air and hydrocarbons and a burner configured to receive the mixed air and hydrocarbons and to generate syngas. The system also includes a cooling system configured to receive the syngas and to cool the syngas to form cooled syngas. The system further includes a collector configured to collect carbon from the cooled syngas as soot. Moreover, the system includes a flare stack configured to receive the cooled syngas and to burn off at least part of the cooled syngas.

The invention provides a storage type time-depth logging method, and relates to the technical field of logging control, and the method comprises the steps: obtaining an encoder, tension and acceleration data, and drawing an encoding curve, a tension curve and an acceleration curve. And receiving an effective movement time interval of single complete drilling rod operation intercepted by the user according to the curve characteristics. And for the original coding curve section of each interval, the total variation and the total duration of the coding value are kept unchanged, and the coding value and the time point are matched again according to the layered scale relationship between the encoder data and the actual displacement of the hook, so that a corrected target coding curve is obtained. And all the target coding curves are converted into time-depth curves according to the depth interval corresponding to each drill rod, and a time-depth file is generated through splicing according to the operation sequence of the drill rod operation event interval. According to the scheme, the technical problem of how to improve the precision of storage type time-depth logging depth measurement and the reliability of the whole system is solved.

The invention discloses a shale gas well middle and later period effusion intelligent diagnosis method based on LSTM and TCN models. The method comprises the following steps: S1, collecting and preprocessing multi-source time sequence data, performing sliding window segmentation, and generating a standardized data set; s2, manually marking an effusion starting point, a critical point and a process intervention point, and generating a training sample set with a label; s3, performing multi-layer expansion causal convolution on the time sequence convolutional network, and extracting multi-scale local features; s4, the modeling of the long-short-term memory network is globally dependent, and output diagnosis and early warning are discriminated; and S5, regularly collecting newly added data and diagnosis results, dynamically updating the sample set, and retraining and optimizing the model. According to the method, intelligent, efficient and real-time diagnosis and early warning of the liquid accumulation state in the middle and later periods of the shale gas well are achieved.

The invention relates to the technical field of geological reservoirs, in particular to a reservoir microcosmic connectivity characterization method based on pore throat boundary intelligent identification, which comprises the following steps of: performing threshold segmentation processing on a rock casting body slice by using image analysis software, and depicting partition images of pore throats and skeleton particles; based on the partition image, combining image analysis software to measure and extract the diameter, perimeter and area of the pore throat, and calculating quantitative parameters of the pore throat structure; constructing a one-dimensional Gaussian distribution model of the nuclear magnetic logging of the reservoir; based on a one-dimensional Gaussian distribution model, establishing a Gaussian collaborative factor for pore structure evaluation; based on the quantitative parameters of the pore throat structure and the established Gaussian collaborative factors, establishing a multi-factor evaluation mathematical method to establish a multi-element evaluation model of the pore structure; and performing multivariate nonlinear fitting on the porous structure multivariate evaluation model, the nuclear magnetic movable fluid saturation and the reservoir pressure measurement fluidity, and constructing a quantitative characterization model for characterizing the reservoir microscopic connectivity. According to the method, the microscopic connectivity of the reservoir can be accurately, rapidly and quantitatively evaluated.

The invention belongs to the technical field of shale oil and gas development, and particularly discloses a multi-source information fused horizontal well casing deformation risk section prediction method, which comprises the following steps: representing shaft crack zone information; establishing a work area construction model and an attribute model; with shaft crack zone information as a constraint, disturbance is performed on crack information predicted based on seismic data, and a three-dimensional crack network model is established; a work area geomechanical model is established, and crustal stress distribution characteristics in the shaft direction of the horizontal well are obtained; a work area casing deformation comprehensive database is established by combining casing mechanical attribute data and work area fracturing construction parameters, and fault zone activity factors are obtained based on the Mohr-Coulomb criterion; and establishing a stratum-fracture-wellbore model, obtaining a mathematical model of fracture zone activity factors and casing deformation degrees, and dividing casing deformation risk grades. According to the method, accurate recognition of the well periphery crack network and advanced pre-judgment of the well periphery crack activation risk and the wellbore casing deformation can be achieved, and guidance is provided for formulating targeted casing deformation prevention and control measures.

A system and method of monitoring a downhole tool implemented in a wellbore that include identifying, from surface parameters, a surface downlink sent from a surface of the wellbore to the downhole tool. The surface downlink indicates a downlink command for the downhole tool to implement. The system and method further include confirming that the downhole tool received the surface downlink based on comparing downhole telemetry data received from the downhole tool to the surface downlink. The system and method additionally include, determining a status of the downhole tool indicated by the surface downlink based on confirming that the downhole tool received the surface downlink.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention provides a dual-control underground cable intelligent water distributor which comprises an upper connector, an upper shell, a connecting body, a lower shell and a lower connector which are connected in sequence, a central pipe is nested in the upper shell, the two ends of the central pipe are in sealed connection with the upper connector and the connecting body respectively, and an adjusting sleeve is nested in the lower shell. The two ends of the adjusting sleeve are sealed with the connecting body and the lower connector respectively, and the adjusting mechanism, the electric control mechanism and the driving mechanism are arranged in a gap between the upper shell and the center pipe respectively. The dual-control underground cabled intelligent water distributor has the beneficial effects that the dual-control underground cabled intelligent water distributor has two measurement and control processes which do not influence each other, the service life of a pipe column can be effectively prolonged, the dual-control underground cabled intelligent water distributor is suitable for an existing cabled intelligent process pipe column, the cable throwing and fishing measurement and control capability is increased on the basis of an existing cabled intelligent working barrel, and the service life of the cable is prolonged. Therefore, the purposes of two control modes of cabling and measuring and adjusting are achieved, and the requirements of multi-layer-section layered water injection, accurate injection allocation and real-time measuring and adjusting are met.

A method of decoding a received communication signal is provided. The method may comprising converting the received communication signal to the frequency domain to provide frequency domain data. The method may further comprise combining amplitudes of one or more selected frequency components of the frequency domain data, the one or more frequency components being selected according to a predefined communication symbol comprising one or more selected frequency tones corresponding to the one or more selected frequency components. The method may further comprise detecting the predefined communication symbol in the received communication signal depending on the combined amplitudes of the one or more selected frequency components of the frequency domain data.

The invention discloses a quick-release modularized tubing head sealing assembly which comprises a bridging carrying part and a Christmas tree detection mechanism, a casing head measuring part sleeved with a bolt is arranged at the output end of the bridging carrying part, and a tubing head sealing part sleeved with a bolt is arranged at the top end of the casing head measuring part. And the output end of the tubing head sealing component is provided with a Christmas tree detection mechanism sleeved by a bolt. According to the device, a sealed cabin shell and a lower arc-shaped inserting sleeve are inserted above a bridging pipeline in an arc-shaped manner, and after a middle backing ring and a middle lining pipe are connected with a bottom ball limiting block, a supporting cabin and a rubber fixing ring in a sleeving and inserting manner, a top backing ring, a ring bolt, an upper sleeving cabin and a top ball limiting block are further connected in a sleeving manner; the structure is a multi-layer annular sleeving installation structure, so that the equipment can be quickly disassembled, assembled and replaced when the equipment needs to be maintained, the use efficiency of the equipment is improved, and the use of the equipment is not influenced.

The invention relates to the technical field of data analysis, in particular to a multi-source data-based concealed vanadium titano-magnetite bed target area delineation method and system, which comprehensively considers various factors possibly influencing drilling, such as geological condition complexity and drill bit wear risk, and has relatively low potential risks in various aspects of trajectory selection. The probability of unexpected conditions such as drill bit damage and borehole wall collapse in the drilling process can be effectively reduced, smooth drilling work is guaranteed, the drilling effect abnormity prediction result and the difficulty abnormity reflect the advantages and disadvantages of the drilling track from different angles, and the drilling efficiency is improved. The drilling effect anomaly prediction considers the influence of various abnormal conditions possibly occurring in the drilling process on the final drilling result, the difficulty anomaly focuses on the drilling difficulty of the trajectory, the two are fused and analyzed, the feasibility and the risk degree of each trajectory are evaluated more comprehensively and accurately, and the prediction accuracy is improved. And the influence degree of the track drilling process on ore deposit target area detection is reduced.

A method can include rendering a graphical user interface to a display; receiving data; responsive to determining boundary locations for formations in the subsurface environment using the data, automatically rendering the boundary locations in the graphical user interface; responsive to determining extended boundary locations in the subsurface environment for a region beyond an end of the borehole using the boundary locations, automatically rendering the extended boundary locations in the graphical user interface; responsive to determining a target location in the region using the extended boundary locations, automatically rendering the target location in the graphical user interface; and, responsive to generation of a trajectory from an end of the borehole location to the target location, automatically rendering the trajectory in the graphical user interface.

A coiled tubing system includes a coiled tubing string configured to couple to a downhole tool to move the downhole tool along a geothermal well, an injector head configured to control movement of the coiled tubing string and the downhole tool along the geothermal well, one or more sensors configured to capture sensor data indicative of a distance traveled by the coiled tubing string along the geothermal well, and a controller configured to control operation of the injector head to move the coiled tubing string and the downhole tool to a measured depth within the geothermal well that corresponds to an intersection of a feature with the geothermal well based on the sensor data received from the one or more sensors.

The invention provides an intelligent monitoring system and method for mine drilling, and belongs to the field of mine drilling monitoring, and the system comprises a multi-mode sensor group which is arranged on drilling equipment and is used for collecting a plurality of drilling parameters in real time, the multiple drilling parameters comprise any multiple of drilling depth data, attitude measurement data of drilling equipment, pressure data of a drilling hole wall, crack image data of the drilling hole wall, vibration data in the drilling process and radial offset of the drilling hole wall; the data transmission module is connected with the multi-mode sensor group and is used for transmitting the drilling parameters to the processing module; the processing module is used for generating drilling state monitoring results according to the drilling parameters, and the drilling state monitoring results comprise any multiple of the drilling depth, the drilling direction, the drilling wall stability and the drilling shape. According to the invention, through synchronous acquisition and fusion analysis of multi-source heterogeneous sensing data, comprehensiveness and reliability of drilling state monitoring are improved.

The invention discloses a shaft operation state intelligent prediction method based on a hybrid deep learning framework, and belongs to the field of petroleum engineering oil-gas field development engineering, and the method comprises the following steps: building a geological sequestration shaft transient temperature-pressure coupling mathematical model, and carrying out the numerical solution; constructing a double sequence feature database covering CO2 injection and leakage working conditions; adopting a parallel architecture to process the double sequence features to construct an improved double attention network; the method comprises the following steps: establishing a hybrid deep learning framework DT-DANet by coupling an improved double attention network and a time fusion Transform; and carrying out collaborative optimization training on the hybrid deep learning framework by adopting a composite loss function, wherein the output of the framework is a wellbore pressure profile, a temperature profile and phase state distribution under different time steps. According to the method, high-precision prediction of shaft multi-parameter dynamic evolution in the CO2 geological storage process is realized.

The invention discloses a multi-mode drilling process multi-dimensional parameter real-time while-drilling monitoring system, belongs to the technical field of while-drilling monitoring, and solves the problems that a monitoring system matched with an existing engineering driller is insufficient in transient feature capture, deficient in data timeliness and diversity, limited in application scene and low in data reliability under a strong vibration working condition. The system comprises a rock drilling subsystem, a hydraulic pump station auxiliary subsystem, a control subsystem and a data collecting and monitoring subsystem. According to the invention, millisecond-level real-time monitoring, data visualization and storage can be carried out on multi-dimensional parameters such as drilling stroke, pressure, flow, frequency, displacement, acceleration and the like in the while-drilling process. The system is high in integration level, quick in response, complete in data and high in adaptability, and reliability, effectiveness, comprehensiveness and timeliness of while-drilling data are remarkably improved.