20680 results about "Petroleum engineering" patented technology

A cable trough for installing cables alongside a railway line or other right of way is described. The cable trough is constructed using longitudinally oriented reinforcing fibers coated with polymeric resin that generally aligns the reinforcing fibers with the length of the cable trough. One or more connector clips may be used to secure respective portions of the cable trough to one another. The cable trough is provided with a cover that may be removably secured to an elongate channel to protect the cables housed within the cable trough. Elbows, corners, tees, and other connectors may be provided to run the cable trough in any desired direction. A method of installing cables in a cable trough is also described.

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention relates to the technical field of coal mine safety monitoring, in particular to an underground coal mine dust concentration monitoring method and system based on multi-modal data fusion, and the method comprises the steps: synchronously collecting dust concentration time sequence data, dust image data, sound wave signal data and environmental parameters through a multi-modal sensor array deployed in an underground coal mine; carrying out preprocessing and feature extraction on the collected data; predicting the decomposed high-frequency and low-frequency component signals by adopting a long short-term memory neural network and a grey Markov model; when the environment humidity is greater than 80%, carrying out light scattering compensation on the predicted value; inputting various predicted values into an improved D-S evidence theory fusion device, and outputting a fusion dust concentration monitoring value; and when the threshold value is exceeded or the temperature and humidity composite condition is reached, an acousto-optic alarm is triggered and a spraying dust-settling device is started. According to the method, the problems of low precision of a single sensor, multi-source data conflict, high-humidity environment measurement deviation and insufficient time sequence and fusion precision in underground coal mine dust concentration monitoring can be solved.

The invention discloses a coal fire area fracture active enhancement detection test system and method, and belongs to the technical field of mine safety and geophysical exploration, and the system comprises a fracture generation module which is used for simulating a heat-force-gas coupling condition of an underground coal fire area, and inducing a coal rock mass test piece to generate a fracture; the fracture detection module is used for synchronously acquiring multi-physics field data of the coal and rock mass test piece in real time; the fracture intervention module is used for injecting a natural medium simulant and an enhanced medium foam gel material into the fracture so as to simulate a real environment and enhance a detection signal; and the intelligent feedback and data fusion module is used for controlling operation parameters of each module, synchronously acquiring detection data, and performing fusion processing, intelligent inversion and four-dimensional visualization on the detection data to realize whole-process monitoring detection of fracture evolution. The invention provides a scientific method for precise delineation and targeted plugging treatment of hidden fractures in a coal fire area, and solves the problems of multiplicity of solution in traditional fracture detection and easy reburning in coal fire treatment.

The invention belongs to the technical field of tunnel and underground engineering intelligent construction and geotechnical engineering informatization, and discloses a high-energy geological environment surrounding rock classification and decision-making method based on digital twinning and multi-source feedback. The problems caused by difficulty in realizing surrounding rock state dynamic sensing, multi-source data fusion classification and construction decision closed-loop linkage in the prior art in a high-energy geological environment are solved. The method comprises the following steps: firstly, constructing a tunnel three-dimensional geology-structure digital twinborn body based on initial survey data; in the construction process, multi-source data such as geology, construction disturbance and surrounding rock response are collected in real time through the Internet of Things technology and mapped to the digital twinborn body, and virtual-real synchronous updating is achieved. And then, constructing a deep learning-parameter inversion hybrid model on the basis of the multi-source fusion data, outputting a dynamic surrounding rock classification index DRCI and key mechanical parameters, inputting the DRCI and the key mechanical parameters into a multi-objective optimization module, and giving a self-adaptive drilling and blasting scheme. And finally, reversely correcting the model through a construction feedback result to realize closed-loop self-learning.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention provides an oil seal press-fitting abnormity real-time detection method based on pressure displacement curve analysis, and relates to the technical field of oil seal assembly control. Pressure displacement data acquisition is executed and a real-time pressure displacement curve is fitted when press-fitting equipment is started; based on displacement progress threshold triggering stage switching of the time sequence displacement data, an association stage characteristic curve is called from the oil seal press-fitting reference template; comparing the characteristic curve in the association stage with the real-time pressure displacement curve to solve a real-time curve deviation vector; and according to the real-time curve deviation vector mapping physical failure abnormal mode, self-adaptive adjustment of oil seal press-fitting process parameters is triggered. The technical problems that in the prior art, a continuous press-fitting process is adopted for oil seal assembly, rubber stress concentration is caused, hydraulic locking is formed due to lubricant retention, and the press-fitting depth is out of control are solved. Physical failure prevention in the oil seal assembly process is achieved, it is guaranteed that the press-fitting depth meets the technological specification tolerance in the whole process, oil seal assembly defects are restrained, and the sealing reliability of an engine is improved.

Providing a method for assessing the impact of fragile structures on large-scale fracturing in deep thermal reservoirs "Solution: Based on a precise depiction of the fragile structure of the formation and a study of its mechanical properties, the spatial distribution of the structure is determined, well locations and networks are arranged, fracturing zones are selected, and the fracturing suitability of the formation is comprehensively evaluated. Furthermore, a thermal-fluid-solid coupled damage model and a thermal-fluid-solid-chemical coupled damage model are established to study the activation conditions of the original fragile structure surface, and the fluid filtration characteristics of the structure and the maximum discharge volume that can be safely fractured are evaluated. Based on this, triaxial fracturing physical simulations and numerical simulations are carried out to clarify the influence laws of the fragile structure surface on the expansion and seepage flow of fracture cracks, and to clarify the influence mechanism of the fragile structure on large-scale fracturing of the formation under high temperature and pressure conditions."

The invention relates to the technical field of multi-source information analysis, in particular to a niobium-tantalum metal ore metallogenic evaluation system based on multi-source information analysis, which comprises a geologic structure association module, a mineral distribution interpretation module, a metallogenic environment adaptation module, a spatial feature analysis module and a potential region judgment module. According to the method, by introducing a multi-source data analysis and fusion method, the evaluation precision and comprehensive capability of resource exploration are effectively improved, analysis after information fusion is combined with relevance and spatial modes of different data types, it is ensured that mineralization potential evaluation is more accurate, the limitation of a single data source and manual analysis is overcome, and the method is suitable for popularization and application. According to the method, the identification precision and reliability of the mineralization law are improved, the potential area can be identified more accurately, resource potential analysis is optimized in combination with the adaptability of the mineralization environment, the efficiency and accuracy of determining the mineralization area are remarkably improved, the problems of information isolation and analysis deviation in a traditional method are avoided, and the scientificity and the practical application value of the exploration process are enhanced.

A dynamic prediction method for the residual gas content of a pre-extraction coal seam comprises the steps that on-site working conditions are considered, parameters such as gas content changes and related geological factors and extraction factors are collected, and a dynamic monitoring data set is constructed; screening main control factors based on grey relational degree, and determining the main control factors influencing the residual gas content after extraction; a main control factor of the residual gas content is used as an independent variable, a pre-pumping dynamic correction factor is combined, a dynamic prediction model is established, and actually measured data is adopted regularly to verify and optimize parameters of the dynamic prediction model; dividing a pre-extraction working face into uniform grid units, and calculating the residual gas content of each grid based on a dynamic prediction model; an extraction blind area is judged according to a quantitative standard and is visually presented; and formulating a differentiated drilling optimization scheme according to the blind area distribution. According to the method, the residual gas content prediction model dynamically responding to mining condition changes is constructed, extraction blind area prediction and drilling optimization are achieved, the coal seam gas extraction efficiency can be improved, and gas disasters are reduced.

The invention relates to the technical field of geothermal exploitation, and discloses a geothermal well group intelligent cooperative regulation and control method for geothermal exploitation, which comprises the following steps: collecting multi-source heterogeneous data in real time through multiple types of sensors deployed in a geothermal well group and a surrounding environment, data standardization, time sequence alignment and noise filtering are carried out through a uniform interface and a communication protocol, and a multi-dimensional monitoring database of the whole geothermal exploitation process is constructed; on the basis of multi-source heterogeneous data, historical mining data in a database is monitored in combination with a geologic structure of a geothermal reservoir, a seepage-heat conduction coupling mechanism and multiple dimensions. According to the invention, by deploying multiple types of sensors and constructing a multi-dimensional monitoring database, real-time acquisition and standardized processing of high-precision and multi-source heterogeneous data in the whole geothermal exploitation process are realized, a reliable data basis is provided for subsequent intelligent decision making, and the sensing ability of the system to complex geology and exploitation environments is significantly improved.

The invention discloses a coal seam passive source seismic survey system and method based on deep learning, and relates to the technical field of coal mining safety monitoring and early warning, and the method comprises the following steps: S1, collecting the passive source seismic data of a coal seam area; s2, deep fusion features are extracted; s3, constructing a semi-supervised self-attention prediction network model; s4, extracting coal seam structure prediction parameters; s5, the corrected coal seam structure parameters are extracted; s6, constructing a fast seismic wave forward model, and calculating and outputting an initial model of the coal seam underground structure; s7, constructing a Transform optimization network, and extracting a depth feature vector; and S8, constructing a coal seam structure inversion model, and training iteration by using the improved batch depth Bayesian optimization algorithm. The method overcomes the limitation of manual analysis and slow response in a traditional coal seam seismic survey method, and provides an efficient and accurate solution for safe production of a coal mine.

The invention discloses a deep learning-based middle-deep layer geothermal pipe group multi-field coupling simulation method, which comprises the following steps: carrying out numerical simulation cross mutual verification to obtain a middle-deep layer buried pipe heat pump system model, and carrying out system configuration by adopting a Monte Carlo method to obtain a first parameter environment set; screening the first parameter environment set according to a parameter environment function to obtain a second parameter environment set, performing data augmentation to obtain mid-deep layer geothermal pipe group simulation data, constructing a mid-deep layer geothermal pipe group multi-field coupling deep learning model, and obtaining simulation heat exchange data and prediction coupling heat exchange data; and adaptive grid optimization is carried out to obtain an optimized grid size, and the optimized grid size is adopted to adjust the mid-deep layer buried pipe heat pump system model to output the mid-deep layer buried pipe multi-field coupling model. According to the method, accurate and rapid simulation of the multi-field coupling process of the medium-deep geothermal pipe group can be realized under complex geological conditions, and the method has important practical significance for promoting large-scale development and utilization of medium-deep geothermal resources.

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.

The invention relates to the technical field of gang drills, in particular to a numerical control gang drill device which comprises a machine tool body, a telescopic assembly is arranged in the machine tool body, the output end of the telescopic assembly faces downwards and is connected with an extrusion block, and a storage plate is arranged at the position, below the telescopic assembly, in the machine tool body. One side of the machine tool body is connected with a gang drill plate through an air cylinder, and the side, facing the machine tool body, of the gang drill plate is evenly connected with a plurality of through drill seats. According to the device, a second clamping block and a first laser distance measuring sensor are arranged, the first laser distance measuring sensor can detect the distance value between a drill bit and an extension block at different positions of the drill bit, and the distance value between the drill bit and the extension block can be detected in the mode that the drill bit rotates; the detected distance value is uploaded to a background control system to be compared with a standard data value, and if the detected data value exceeds a standard data value interval, it is indicated that the drill bit deforms.

The invention provides a prediction method for a sudden change type signal of gas dissolved in oil. The prediction method comprises the following steps: firstly, acquiring a time sequence signal of transformer gas monitoring; processing the time sequence signal according to a variational mode decomposition algorithm to obtain a plurality of mode components; according to the variational mode decomposition algorithm, the sum of confusion entropies of all mode components is used as an objective function of parameter optimization; and finally, according to a pre-established prediction model, performing prediction and superposition reconstruction on each modal component to obtain a prediction result of the abrupt change type signal of the gas dissolved in oil. According to the method, the VMD decomposition architecture optimized by the frost ice algorithm is introduced, the non-stationarity of the signal is quantified and obviously reduced through the chaos entropy index, higher prediction precision and robustness of the gas signal in the mutation type oil are realized, and the method has obvious innovativeness and superiority in the field of transformer fault trend prediction.

The invention discloses a sandstone reservoir deep acidification long-acting permeation enhancement effect numerical simulation method, and relates to the technical field of rock reservoir acidification transformation processes, and the method comprises the steps: collecting real-time sensor data and geological exploration data of a sandstone reservoir, and making a preliminary acidification operation plan; according to the preliminary acidification operation plan, in combination with real-time data feedback, the preliminary acid liquor concentration, the preliminary injection rate and the preliminary acid liquor injection temperature are optimized, and acidification parameters are output; based on real-time acidification operation monitoring data and sampling feedback data, a multi-field coupling numerical model is constructed and calibrated, the long-acting evolution track of the permeability of the acidified reservoir is simulated, the effective permeation increasing period and the final permeation increasing amount are evaluated, and an acidification long-acting permeation increasing effect simulation report is output. According to the method, the real-time regulation and control capability of acidification operation is improved, a scientific basis is provided for evaluating the economic validity period of acidification transformation, and an innovative solution is provided for efficient development of a deep sandstone reservoir.

The invention discloses a coal mine goaf fire monitoring method and system based on multi-source data fusion, and relates to the technical field of coal mine goaf fire monitoring, and the method comprises the steps: collecting the concentration, concentration and air pressure of each wireless sensing node in a coal mine goaf in real time; according to the concentration of each node, a dual-parameter dynamic coupling gradient field traceability algorithm of fusion / gas expansion and chemical reaction mechanism is adopted to calculate a fire source coordinate; performing anomaly detection according to the concentration of each node, and positioning an air leakage point by adopting a three-dimensional oxygen concentration gradient field positioning algorithm and combining air pressure compensation for an oxygen concentration anomaly point; based on goaf point cloud data, a goaf three-dimensional grid model is constructed, and a fire source spreading path, an air leakage channel and gas concentration distribution are dynamically and visually displayed in combination with multi-source monitoring data and fire source and air leakage point positioning results. The goaf fire prevention and control system can achieve all-around prevention and control of goaf fire.

The invention relates to the technical field of ship hydraulic monitoring, and discloses a marine ship hydraulic oil cylinder state monitoring and sealing performance early warning system. A multi-source sensing data acquisition module of the system synchronously obtains multi-dimensional time sequence operation data of an oil cylinder through a vibration sensor, a pressure sensor and a temperature sensor; the data preprocessing fusion module is used for carrying out noise suppression, timestamp alignment and multi-modal fusion on the data to generate a standardized oil cylinder state feature sequence; the sealing characteristic depth extraction module is used for analyzing sealing cavity pressure fluctuation, piston rod displacement oscillation and sealing piece deformation trend characteristics from the characteristic sequence; the multi-scale state evaluation module is used for calculating a sealing performance degradation gradient and a dynamic operation stability coefficient according to the characteristics; and the early warning decision generation module is used for generating a sealing failure risk level and an early warning control instruction based on the calculation result. According to the system, stable operation of the oil cylinder is guaranteed, and the maintenance cost and the safety risk are reduced.

The invention discloses a tunnel or mine water gushing space-time prediction method and system coupled with a hydrodynamic numerical model, and the method comprises the steps: outputting multi-source data based on an identified and verified underground water numerical model, complementing the missing of measured data, quantifying the difference between the permeability characteristics of a fault and a normal stratum, and coupling the difference to a data system, and tunnel or mine excavation space data are merged. And constructing an LSTM-isolated forest-K neighbor regression coupling model, and configuring a multifunctional module to realize multi-scene data co-training. The preprocessed multivariate time series data is divided into a training set and a test set, hidden features are extracted through a coupling model, anomaly detection results are fused, a residual error correction model is synchronously trained, and hyper-parameters and weights are adaptively optimized according to multi-engineering prediction error feedback. And based on the trained coupling model, carrying out synchronous water gushing space-time prediction by adopting a window rolling strategy, and outputting prediction data meeting engineering precision in combination with residual correction. And reliable technical support is provided for safety prevention and control of engineering construction.

Disclosed is a physical simulation experiment system for vapor-liquid multiphase seepage in an underground gas storage facility in a depleted gas reservoir, including: a rock sample sealing, heating, and insulation system, a triaxial stress and confining pressure loading system, an oil-gas-water injection and pore pressure supply system, a vapor-liquid-solid three-phase separation and measurement system, an optical micro / nanofiber gas detection and monitoring system, a distributed fiber optic sensing system, and a gas supply and leakage alarm system, where the rock sample sealing, heating, and insulation system is used for accommodating and heating a rock sample; and the oil-gas-water injection and pore pressure supply system performs oil-gas-water injection into the rock sample and pore pressure maintenance. The present application aims to overcome the bottleneck problem of evaluating the dynamic sealing integrity of underground gas storage under alternating loads caused by injection and withdrawal.

The invention provides an oil-immersed transformer hot-spot temperature prediction method and system based on a Transform model, and belongs to the technical field of transformer hot-spot temperature prediction, and the method comprises the steps: obtaining key operation parameter data of an oil-immersed transformer, and carrying out the data preprocessing; and inputting the key operation parameter data of the oil-immersed transformer after data preprocessing into the transformer hot-spot temperature prediction model for prediction. On the basis of a self-supervised pre-training mechanism of mask reconstruction, time sequence features are learned by using label-free data, so that the feature extraction capability and generalization performance of the model are improved.

The invention discloses a multielement multiphase fluid displacement coalbed methane 4D visual simulation experiment system and a multielement multiphase fluid displacement coalbed methane 4D visual simulation experiment method. The system comprises a gas-liquid mixed injection system, a Kr adsorption system, a visual triaxial core holder system, a CT scanning system and a tail gas analysis and treatment system. According to the experimental method, Kr similar to CH4 in dynamic diameter and other parameters is selected as a CT tracer to replace CH4, and 4D visualization of coal body internal pore and fracture structure evolution and Kr concentration field displacement migration process can be realized through three-dimensional reconstruction of CT images at different displacement moments and registration, gray correction and normalized concentration calculation of the images. The problem that the gas migration rule in the coal body in the displacement experiment is not clear is solved; the dynamic diffusion process inversion of the coal bed gas in the coal bed difficult to extract is realized by utilizing the distribution characteristic evolution of the Kr gas in the coal body, so that the in-situ coal body fissure structure in the coal sample and the dynamic migration law of methane in the displacement process are obtained, and the method has great significance for revealing the displacement development mechanism of unconventional natural gas such as the coal bed gas.

The invention belongs to the technical field of petroleum and natural gas development engineering, discloses a shale reservoir three-dimensional dessert prediction method based on physical-data dual drive, and solves the defects of an existing shale reservoir dessert evaluation method in the aspect of space continuity. The prediction method comprises the following steps: (1) constructing a three-dimensional attribute field, and forming a multi-channel standardized three-dimensional data volume through voxelization processing; (2) fusing multi-parameter attributes of the shale reservoir and constructing a multi-channel three-dimensional feature body; (3) constructing a dessert prediction model based on the physically constrained space-attribute joint attention three-dimensional convolutional network; and (4) calculating a comprehensive dessert index, performing dessert grading and cause analysis, analyzing and quantifying the contribution degree of each attribute to dessert grading, and analyzing the dessert connectivity and the advantage extension direction. The predicted productivity potential and fracturing potential dessert field keeps reasonable gradient change and continuous distribution in the inter-well area, and the inter-well prediction error is small.

The invention provides a mine slope risk assessment method and system based on multi-source data space-time fusion, and relates to the technical field of mine monitoring, and the method comprises the steps: obtaining geological and engineering data, and constructing a basic model representing a slope three-dimensional space structure; collecting multi-source heterogeneous monitoring data on a mine slope field, and aligning and fusing the multi-source heterogeneous monitoring data with the basic model in a space-time dimension to generate a continuous dynamic parameter field; and setting a data fusion model, and taking the basic model and the dynamic parameter field as input to obtain a four-dimensional space-time fusion risk field. According to the method, the basic model representing the three-dimensional space structure of the slope is constructed, and a unified space-time bearing base is provided for all heterogeneous monitoring data, so that originally isolated data such as GNSS displacement, deep displacement, blasting vibration and underground water level can be fused in a unified coordinate system; the problem of data splitting in the prior art is fundamentally solved, and integral and integrated expression of slope engineering and geological conditions is formed.

The invention discloses a fluid distribution dynamic regulation and control method and device based on a shale reservoir microstructure, and relates to the technical field of oil and gas exploitation. The method comprises the steps that microstructure parameters of a target shale reservoir are obtained through a core experiment, and a multi-scale model is constructed based on the microstructure parameters; determining control equations of different physical fields of the multi-scale model; coupling the control equations of different physical fields, and resolving through a finite element method; performing a numerical simulation experiment based on the multi-scale model and the resolving result to determine regulation and control parameters; the regulation and control parameters are input into the multi-scale optimization model to optimize the regulation and control parameters, and a regulation and control strategy is obtained. The problems that an existing displacement experiment is single, the complex interaction process under the actual mining environment is not comprehensively considered, and the guiding effect on mining work is limited are solved. Business personnel can be guided to formulate a development scheme more scientifically and accurately, the mining efficiency and the success rate are improved, the development cost can be reduced, and meanwhile economic benefits are improved.

The invention provides a tunnel three-dimensional geologic body modeling method and system based on in-hole camera shooting and while-drilling lithology perception, and relates to the technical field of tunnel and underground engineering and intelligent geological information processing.The method comprises the steps that firstly, a drill hole wall optical image sequence and while-drilling engineering parameters are synchronously collected and preprocessed respectively; then establishing a depth-time mapping relation to realize accurate alignment of the multi-source data in a depth domain; then fracture intelligent identification and parameter extraction are realized through an improved U-Net network, and lithology classification is completed by using an XGBoost model; feature level and decision level fusion is carried out on the fracture features and the lithologic features, and rock mass quality grades are generated; and finally, constructing an integrated geologic model fusing lithologic distribution and a three-dimensional fracture network by adopting an implicit modeling and discrete fracture network technology. According to the method, the problem of splitting of in-hole visual information and while-drilling physical information is solved, automation and precision of geological recognition are improved, and reliable geological information support is provided for tunnel construction safety and support design.

A fluid end made of a housing and a plunger reciprocating therein. The plunger has a hollow bore formed therein, and a valve assembly situated on one end of the plunger. The plunger may have a sleeve situated within the bore that aids in the installation of the valve assembly. The plunger may have a second bore extending perpendicularly from the hollow bore. The first bore may be connected to an attachment piece which connects to a conduit. The conduit may then be connected to a manifold which is configured to provide a fluid to the plunger's bore. The housing may be made of a static section and a plurality of dynamic sections. The dynamic sections may thread into the housing. The static section may be made of two pieces to aid with installation of a dynamic seal.

Disclosed in the present invention is a rock porosity prediction method based on the inversion of few-shot while-drilling data. While-drilling parameters of a drilling rig during a laboratory or on-site drilling process are collected, wherein the while-drilling parameters comprise parameters such as torque M, thrust force F, rotation speed N, penetration rate V, drill pipe amplitude A and vibration acceleration a; different types of while-drilling parameter data which have been subjected to multiple instances of denoising processing are inputted into a BP-GA model for outlier removal, data augmentation and iterative calculation, so as to form a new while-drilling data set; and while-drilling parameter time-frequency domain feature maps of rocks having different porosities are inputted into a VG-CNN prediction model, an inversion model between while-drilling parameter time-frequency domain features and the porosities is obtained by means of training and learning, and finally inversion prediction is performed on the rock porosities on the basis of on-site real-time while-drilling parameters. The present method enables fast, accurate and quantitative inversion and prediction of rock porosities on the basis of a small amount of borehole while-drilling data.

The invention discloses a wind power generation equipment gearbox oil state detection method and system, and the method comprises the steps: obtaining an oil sample, carrying out the quantitative analysis of solid, liquid and gas pollutants through an oil pollution degree analysis model, and processing the parameters such as kinematic viscosity through an oil physicochemical property analysis model; and after the tensors of the two results are fused, feature decoupling and deep feature mining are performed, and finally, the oil state is judged by a state evaluation decision model. The system is correspondingly provided with six units such as a sample acquisition unit and a pollution analysis unit to realize the functions. According to the scheme, the defects that traditional detection is single and correlation analysis is lacked are overcome, a certain index is not independently detected any more, pollution and physicochemical property information is comprehensively integrated, the relation between the pollution and the physicochemical property information is deeply analyzed through tensor fusion, feature decoupling and other technical means, accurate evaluation of the oil state of the gearbox is achieved, and the detection accuracy is improved. And misjudgment caused by one-sided detection or no consideration of mutual relations is effectively avoided.