8538 results about "Well drilling" patented technology

The invention presents a system for geospatial topographical modeling and real-time system control to be used with subterranean slurry injection technology. The system consists of a predictive modeling subsystem, which processes land characteristics to generate optimal site plans for drilling and injection. The real-time control subsystem implements these plans, dynamically adjusting slurry compositions and injection parameters based on sensor feedback. A feedback loop between the subsystems allows continuous refinement of both predictive models and operational controls, enhancing the precision with which the technology shapes the ground and the efficiency with which the technology sequesters carbon.

The invention discloses a drilling key parameter automatic regulation and control method based on an improved DDPG algorithm. The method comprises the following steps: collecting and preprocessing drilling data, and constructing a complete data set for training; a drilling speed (ROP) time sequence prediction model is constructed and trained on the basis of a self-attention mechanism (self-attention); building a simulation environment by using the trained ROP prediction model, and defining a state space, an action space and a multi-target reward function of a reinforcement learning DDPG algorithm model; a deep deterministic strategy gradient algorithm (DDPG) is adopted to train a reinforcement learning model, through an Actor-Critic network structure, an intelligent agent learns an optimal bit pressure (WOB) and rotating speed (RPM) regulation strategy in a simulation environment, and dynamic optimization of the drilling speed (ROP) is achieved. According to the method, through automatic and intelligent parameter regulation and control, the efficiency and stability of the drilling process are improved, the drill bit wear rate and the operation cost are reduced, and the method has good adaptability and multi-target optimization capacity and is suitable for drilling operation under the complex stratum condition.

Provided is a two-part drilling and running tool, a well system, and a method for forming a well system. The two-part drilling and running tool, in one aspect, includes a conveyance, a smaller assembly coupled to an end of the conveyance, and a larger bit assembly slidably coupled to the conveyance, the smaller assembly and larger bit assembly configured to slidingly engage one another downhole to form a combined bit assembly. The two-part drilling and running tool, according to this aspect, further includes a one way mechanism coupled between the smaller assembly and the larger bit assembly, the one way mechanism configured to allow the smaller assembly and larger bit assembly to axially slide in one direction relative to one another and prevent the smaller assembly and larger bit assembly from axially sliding in an opposite direction relative to one another.

The invention discloses a deepwater well drilling overflow early warning device and method, and relates to the technical field of petroleum engineering, the deepwater well drilling overflow early warning device comprises a data acquisition module, an environment compensation module, a data processing module, a data processing module and a data processing module, the data acquisition module is used for acquiring data during well drilling, and the data comprises underground bottom hole pressure, temperature, acoustic logging data, gamma logging data, underwater gas content data and overwater well fluid flow data; the environment monitoring module is used for monitoring environment changes and dynamically correcting data acquired by the data acquisition module by applying a real-time correction algorithm based on a neural network, and the data processing and analyzing module is used for cleaning the data of the data acquisition module, extracting features and transmitting the features to the early warning system module. Accurate environment data are obtained through the environment compensation module, the data are processed through a real-time correction algorithm, the influence of external interference such as water flow fluctuation and temperature change on sensor reading can be eliminated, and therefore the accuracy and reliability of the data are improved.

The invention provides an intelligent drilling speed prediction method based on physical feature guidance and multi-source information fusion, and relates to the technical field of intelligent drilling speed prediction, and the method specifically comprises the following steps: collecting multi-source heterogeneous data from a drilling real-time database, a logging system, a logging system and a geological database; constructing a dual-channel deep learning prediction model, wherein the dual-channel deep learning prediction model comprises a dual-channel convolution feature extraction module, a feature fusion module, a time sequence fusion module, a time sequence modeling module and a full connection layer which are connected in sequence; obtaining a predicted drilling speed by using a dual-channel deep learning prediction model; a joint loss function is constructed by considering a data driving error and a physical constraint error, an error is calculated according to the joint loss function, and network parameters are updated through back propagation; carrying out loop iteration training until convergence; and the trained dual-channel deep learning prediction model is used for drilling speed prediction. According to the technical scheme, the problems that in the prior art, a mechanism model is insufficient in precision, and a data driving model is poor in reliability are solved.

A well location and well trajectory determination method, comprising: on the basis of a stratum pressure test, and a flow phase constraint of each stratum, establishing a full-stratum-series three-dimensional rock mechanical parameter field model, so as to obtain full-stratum-series rock mechanical parameters; on the basis of a geological structure, and a stratum pressure field of each stratum, establishing a three-dimensional full-stratum-series stress field prediction model, so as to acquire full-stratum-series three-dimensional stress field parameters; on the basis of the full-stratum-series rock mechanical parameters, the full-stratum-series three-dimensional stress field parameters and a three-dimensional natural fracture model, establishing a natural fracture activity prediction model, so as to evaluate fracture activity; and determining a well location and a well trajectory on the basis of a three-dimensional original field geostress model, a three-dimensional fracturability prediction model, the fracture activity and a three-dimensional collapse pressure prediction model. By means of the method, the effects of drilling speed increases and modification and production increases can be achieved in complex oil and gas field blocks. Further provided are a drilling speed increase method, a modification and production increase method, a well location and well trajectory determination apparatus, an electronic device, and a computer-readable storage medium.

The invention provides a real-time drilling tool jamming early warning method based on multi-modal data fusion and a graph neural network. The method is suitable for drilling tool jamming monitoring in drilling operation. According to the method, multi-modal data such as vibration, pressure and temperature are collected in real time through multiple sensors, and key features are extracted and fused into unified data representation after denoising, missing value filling and standardization processing. And then constructing a graph neural network model, capturing the space-time correlation between the sensors by using a graph convolutional network, and predicting the drilling tool jamming risk. After real-time data access, corresponding early warning is triggered based on the risk probability of drilling tool jamming, and an early warning threshold value is dynamically adjusted to adapt to different geological conditions. According to the invention, the accuracy and the real-time performance of early warning of jamming of the drilling tool are improved, and the adaptability and the robustness of the system are enhanced.

The invention provides a while-drilling formation pressure early warning method and device based on model prediction, equipment and a medium, and relates to the technical field of low-carbon mining, and the method comprises the steps: in a pre-drilling stage, based on seismic data, adjacent well historical data and design data, analyzing and generating prediction risk data of a target monitoring well; in the drilling process, actual formation pressure data are calculated in real time by using drilling data collected for a target monitoring well in real time, and the actual formation pressure data are compared with the predicted risk data; in the drilling stage, a pressure safety window is depicted based on the predicted risk data, the cycle equivalent density of the target monitoring well calculated in real time is compared with the safety window, and the drilling safety risk is recognized and analyzed; and after drilling is finished, summarizing the predicted risk data, the actual formation pressure data and the drilling safety risk, and updating and iterating the model algorithm through the summarized data. The method has the effect of improving the accuracy of formation pressure early warning while drilling in the petroleum drilling process.

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.

A method for constructing a high fidelity logging while drilling (LWD) log that includes obtaining a temporal well depth log and a plurality of temporal LWD logs from a drilling operation. The method further includes obtaining a temporal record of operational drilling parameters from the drilling operation. The method further includes determining, using a first machine-learning model, a temporal history of the drilling operation and constructing, using the temporal well depth log and the plurality of temporal LWD logs, a plurality of temporal property logs at each depth in the plurality of depths. The method further includes processing the plurality of temporal property logs with, at least, a second machine-learning model to form a plurality of corrected temporal property logs and aggregating the plurality of corrected temporal property logs to form a plurality of high fidelity LWD logs.

The invention provides an intelligent drilling guide method and system for a petroleum drilling tool face, and relates to the technical field of intelligent control, and the method comprises the steps: determining a first target point, a second target point and a third target point in the process of tracking a target azimuth angle according to a dynamic adjustment instruction; constructing a dynamic triangle through the first target point, the second target point and the third target point, and taking the barycentric coordinate of the dynamic triangle as an azimuth angle correction value; correcting forward and reverse rotation angles and periodic parameters of the drill string according to the azimuth angle correction value so as to obtain a corrected dynamic adjustment instruction; and controlling a drill string to act based on the corrected dynamic adjustment instruction, so that the underground tool surface continuously approaches the target azimuth angle through closed-loop iteration adjustment. The control efficiency and stability of the tool face under the complex working condition can be improved.

The invention relates to a preparation method and application of a composite leakage-proof agent while drilling, and belongs to the technical field of petroleum drilling engineering. The composite leakage-proof agent while drilling is prepared from the following components in percentage by mass: 20-45% of nano composite particles, 15-35% of a multi-stage fiber reinforced network material, 10-20% of an intelligent response blocking agent, 5-10% of a high-temperature-resistant additive, 1-5% of a dispersing agent and the balance of deionized water, wherein the sum of the contents of all the components is 100%. According to the composite leak-proof agent while drilling provided by the invention, the temperature-sensitive resin with a core-shell structure is introduced, so that a three-dimensional synergistic mechanism of nano-composite particle filling-multistage fiber network bridging-intelligent response curing is realized, and efficient and environment-friendly plugging of an ultrahigh-temperature and high-pressure stratum is realized. The success rate of one-time plugging is increased from 45% to 92%, the average operation time is shortened by 60%, and an efficient solution is provided for deep and ultra-deep drilling.

The invention provides a method and equipment for optimizing drilling parameters and a storage medium, and relates to the technical field of oil-gas exploration. The method comprises the steps that a speed increasing target is determined according to drilling speed information of a current drilling stratum, and a plurality of drilling parameter optimization combinations are determined according to a target mechanical drilling speed prediction model, a target torque prediction model and drilling parameter constraint conditions; determining a plurality of decision-making indexes according to the predicted mechanical specific energy information and the to-be-optimized drilling parameters, and determining a target decision-making index according to the current drilling stratum; target decision index information of each drilling parameter optimization combination is determined, and a target drilling parameter optimization combination is determined according to the multiple pieces of target decision index information. According to the method provided by the invention, the technical effect of accurately optimizing the drilling parameters when the drilling speed is increased is realized.

Drilling mud is flowed through a valve assembly that is set at a particular open amount, measurements are taken of mud flowrate and a force exerted by the drilling mud onto a portion of the valve assembly, and density of the drilling mud is estimated based on historical data. The historical data is generated by directing multiple flows through the valve assembly, where the flows are at different flowrates, and at each flowrate different drilling muds having different densities are directed through the valve assembly. For each flow the valve assembly is adjusted to different open amounts, and a corresponding force exerted by the flow onto the valve assembly is measured. An example of the valve assembly is a mud pulser with a plug member that engages an orifice opening, a sensor in the pulser measures force exerted by the flow of mud.

The invention relates to the technical field of drilling parameter control, in particular to an exploration drilling parameter control method and system.The method comprises the following steps that a drilling target and a target priority corresponding to the drilling target are set, and the target priority indicates the importance of the drilling target; acquiring various measurement data in the drilling process, and processing the various measurement data; according to the processed multiple measurement data, the current stratum characteristics and the current drilling tool operation state are recognized; according to geological information in the drilling process, the drilling target and the target priority are adjusted; according to real-time geological information and a drilling state, a drilling target and a parameter control strategy are dynamically adjusted, so that the method has the advantages of better adapting to complex stratum changes, optimizing tradeoff among multiple targets and improving drilling self-adaptability and control precision.

A system and method for accessing drilling records. A semantic web for drilling records is built, wherein building includes converting drilling records retrieved from a database into a Resource Description Framework (RDF) format and storing the RDF formatted drilling records in the semantic web. Transfer learning is applied to drilling domain knowledge to obtain a large language model (LLM) with drilling domain knowledge. A schema is applied to unstructured records to extract information from unstructured records and the extracted information is then stored in RDF format to the semantic web. The LLM is configured to query the semantic web and to manipulate RDF formatted data within the semantic web.

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 the technical field of engineering early warning, in particular to a drilling engineering early warning method and system, and the method comprises the following steps: collecting a plurality of operation parameters of drilling equipment in real time based on a drilling engineering environment, screening the data, carrying out the comparative analysis of the parameters, evaluating the deviation between the current operation parameter and the conventional operation data, and carrying out the early warning of the drilling engineering. And obtaining equipment operation deviation information. According to the method, the operation parameters of the drilling equipment are collected and processed, key parameters such as the drill bit speed and torque are monitored in real time, deviation with conventional data is found in time, the abnormal condition of the equipment can be quantitatively evaluated by analyzing the frequency and duration of the operation deviation of the equipment, and the stratum data aggregation and trend analysis are combined. The safety of drilling operation is enhanced, the stability of a drilling site can be evaluated by extracting key features such as stratum density and rock hardness, risk classification is performed accordingly, risk early warning is achieved, and potential accidents are effectively avoided.

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 provides a temperature-resistant and salt-resistant cross-linking tackifying filtrate reducer for a soil-phase-free water-based drilling fluid as well as a preparation method and application of the temperature-resistant and salt-resistant cross-linking tackifying filtrate reducer, and belongs to the technical field of drilling. The temperature-resistant and salt-resistant cross-linking tackifying filtrate reducer for the soilless phase water-based drilling fluid comprises the following raw materials in parts by weight: N, N-dimethylformamide, a cross-linking agent and a cross-linking agent, the invention relates to a water-based adhesive which is prepared from the following components in parts by weight: 15 to 25 parts of N, N-dimethylacrylamide, 1 to 10 parts of 3-[(3-methacrylamidopropyl) dimethylammonium] propane-1-sulfonate, 1 to 10 parts of long-chain alkyl polyoxyethylene ether methacrylate, 0.005 to 0.015 part of macromolecular cross-linking agent, 0.03 to 0.08 part of initiator A and 80 to 100 parts of deionized water. The tackifying filtrate reducer can resist the temperature of 180 DEG C, can resist NaCl to be saturated, has excellent tackifying, shear-improving and filtrate-reducing effects under high-temperature and high-salt conditions, improves the rheological property and the filtrate-reducing property of drilling fluid, and ensures safe and efficient drilling of a deep well.

The invention relates to the field of measurement while drilling in drilling operation, in particular to a small-diameter resistivity while drilling measuring device which comprises a drill bit installed at the bottom of a drill rod, a main shaft rotationally arranged in the drill rod, hinged plates symmetrically arranged at the upper end of the drill rod, and an annular cavity formed in the drill rod. Annular frames with U-shaped sections are symmetrically arranged in the annular cavity in a sliding manner, a receiving end and a transmitting end are respectively mounted in the annular frames which correspond to each other up and down, and mounting units for limiting the transmitting end and the receiving end are arranged in the annular frames. The high-efficiency detection of measured objects with different depths and angles in a complex geological environment is realized, the vibration generated in the drilling process of the drill bit is effectively absorbed, the transmitting end and the receiving end are ensured to work in a stable environment, the measurement error caused by the vibration is avoided, and the detection precision and reliability are greatly improved.

The invention provides a well drilling automatic control system which comprises an input establishing module used for inputting well track design parameters and establishing a well drilling system dynamic model; the target trajectory planning module is used for verifying stratum parameters according to the actual drilling data and planning the target trajectory of the current drilling section based on the verification result by using the drilling system dynamics model to obtain the target trajectory; the control quantity feed-forward calculation module is used for calculating the control quantity of the drilling machine by adopting a feed-forward algorithm based on the target track parameters, controlling the drilling real-time calibration module of the drilling machine, and correcting the control quantity in real time based on real-time evaluation of drilling errors in the drilling process; and the data output module is used for outputting logging data in the drilling process of the column after the preset drilling distance of the column is reached. Full-intelligent operation in the drilling process is achieved, and the precision, safety and oil and gas recovery efficiency of drilling operation are effectively improved.

The invention is suitable for the technical field of petroleum drilling engineering, and provides a drilling overflow early recognition method based on self-supervised learning, which comprises the following steps: constructing drilling multidimensional parameters into a time sequence sample by using a sliding time window, and inputting the time sequence sample into a multi-branch consistency constraint twinning network (MCSiam); the MCSiam firstly performs data enhancement on a sample, then extracts a local spatial-temporal feature and a long-term dependency relationship through an encoder composed of CNN-LSTM, and performs multi-branch comparative learning, consistency constraint optimization and predictor prediction and model optimization, and the finally trained model is used for early recognition of drilling overflow. The MCSiam is superior to a traditional method and other deep learning models in performance indexes such as accuracy, the calculation complexity is low, the applicability is wide, overflow early recognition is achieved, efficient and reliable technical support is provided for petroleum drilling safety and well control safety, and the method has good practicability and innovativeness in the field of drilling overflow early recognition.

The invention relates to a wellbore multiphase flow model numerical solution and gas-liquid distribution state inversion method and system, and belongs to the technical field of petroleum engineering, and the method comprises the steps: 1, constructing and training a physical information neural network for drilling wellbore multiphase flow dynamic simulation and overflow gas distribution state inversion; determining input and output of the physical information neural network; determining a loss function of the physical information neural network; training a physical information neural network; 2, designing an adaptive optimization algorithm, optimizing the final solution precision and convergence speed of the physical information neural network, and obtaining an adaptive physical information neural network; designing an adaptive activation function; designing a self-adaptive sampling mechanism based on residual errors; 3, based on the self-adaptive physical information neural network, numerical solution and gas-liquid distribution state inversion of the shaft multiphase flow model are achieved. According to the method, the problem that a traditional numerical method usually needs high-precision grid division and a large number of computing resources is effectively solved.

The invention relates to the technical field of oil and gas well drilling, in particular to an experimental system for measuring the dielectric constant of drilling fluid, which comprises a reaction kettle body, a pressure unit, a temperature unit and a dielectric constant sensor. In the oil-gas well drilling operation process, whether the overflow phenomenon occurs in an oil-gas well or not is judged by monitoring the fluctuation of the dielectric constant of drilling fluid in real time, but the fluctuation of the dielectric constant can also be caused by changes of physical conditions such as pressure and temperature of the drilling fluid, and if the fluctuation of the dielectric constant caused by the changes of the physical conditions is not eliminated, the overflow phenomenon is judged. If so, the accuracy of overflow monitoring is influenced. In the application, the pressure unit and the temperature unit are used for respectively changing the pressure and the temperature of the drilling fluid so as to obtain the change relationship between the pressure and the temperature of the drilling fluid and the dielectric constant; therefore, whether the fluctuation of the dielectric constant of the drilling fluid in the oil and gas well is caused by the change of physical property conditions such as pressure and temperature or not is judged according to the change relation, and the accuracy of overflow monitoring is further improved.

The invention relates to the technical field of data, and discloses a logging data intelligent processing method. The method comprises the following steps: acquiring data through a multi-source logging device and preprocessing the data to form a result set; identifying and classifying stratum features according to the result set; analyzing oil and gas parameters based on the stratigraphic map and the result set; identifying risks by using the oil-gas evaluation table and the result set and establishing an early warning mechanism; the reservoir physical property is evaluated according to the oil and gas evaluation table and the early warning mechanism; and integrating the stratigraphic map, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure prediction enrichment area. According to the method, full-process intelligent processing from acquisition and preprocessing of multi-source heterogeneous logging data to stratum identification, oil and gas evaluation, drilling risk early warning, reservoir assessment and oil and gas reservoir description is realized, and the problems of data space-time mismatching, parameter isolated analysis, high evaluation subjectivity, early warning lag and the like in a traditional method are solved; and the efficiency, accuracy and systematicness of logging data processing are improved.

The invention relates to the field of intelligent remote control, in particular to a digitization-based intelligent remote control method and a digitization-based intelligent remote control system for a drilling machine. The system monitors and collects drilling parameter data in real time through a sensor network, and transmits the drilling parameter data to a remote control server through a wireless communication network. In a remote control server, a data processing and analysis algorithm based on artificial intelligence and deep learning is used to carry out time sequence analysis and collaborative association feature description on the drilling parameter data, so as to capture context semantics among time sequence features of each drilling parameter. The bit pressure time sequence semantics are used as query features to query and match the optimal bit pressure recommendation decoding value from the other drilling parameter time sequence collaborative association features, and the bit pressure recommendation decoding value is compared with the bit pressure value at the current time point to generate a bit pressure remote regulation and control instruction, so that the bit pressure remote regulation and control instruction is quickly adapted to the change of geological conditions in the drilling process; and remote intelligent control of the drilling machine is realized.

The invention provides a drilling risk determination method and device. Before specific implementation, a graph auto-encoder is introduced and used, and a preset detection model which is based on physical constraints and has a good application effect is obtained through unsupervised learning and training. In specific implementation, current target logging data of a target well and a logging data set of a current time period can be firstly obtained; determining a current working condition according to the target logging data; according to the current working condition, the logging data set of the current time period and a preset geological-engineering pre-drilling evaluation profile, a target dynamic threshold value which aims at a current target well and is based on working condition constraints and considers the change condition of data processed in the current time period before graph self-coding is determined; processing the target logging data by using a preset detection model to obtain a corresponding target reconstruction error; and detecting whether a drilling risk exists according to the target reconstruction error and the target dynamic threshold. Therefore, the drilling risk can be accurately detected and identified, and the false alarm rate is reduced.