1471 results about "Porosity" patented technology

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

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 invention provides a hot dry rock terrestrial heat extraction method capable of efficiently adapting to complex heat storage conditions. The method comprises the steps that a heat-fluid-solid coupling relation in the terrestrial heat exploitation process is established, a dynamic porosity and permeability model is established based on the heat-fluid-solid coupling relation, and a terrestrial heat exploitation model is established based on the porosity and permeability model. According to the method, the heat extraction efficiency and the accumulated heat are remarkably improved, the heat competition effect between the production wells is reduced, the service life of the production wells is prolonged, the mining requirements under the complex geological conditions are met, the prediction precision of the heat-fluid-solid model is improved by dynamically adjusting the reservoir characteristics such as porosity and permeability, and the mining efficiency is improved. The problems of high thermal competition, inaccurate prediction and low mining efficiency in the prior art are solved, and a sustainable optimization scheme with high economical efficiency is provided for geothermal exploitation of the hot dry rock.

The invention provides a method for detecting the porosity of ores in a dump leaching process, and belongs to the technical field of detection of the porosity of the ores. The method comprises the following steps: taking a dump leaching ore body, screening, uniformly mixing, splitting, putting into a dump leaching column to obtain the volume of a sample subjected to splitting in the dump leaching column, then performing fine grinding, putting into the dump leaching column to obtain the volume of the sample subjected to fine grinding in the dump leaching column, and then performing image analysis on the sample subjected to fine grinding to obtain the pore volume among sample particles subjected to fine grinding. And finally, converting the three volumes to obtain the porosity of the heap-leached ore. Through screening treatment, the sample representativeness is effectively improved; the ore before superfine grinding and after superfine grinding is introduced into a dump leaching column, the sampling uniformity is controlled, and the accuracy and stability of data are improved through the in-pile process under the same test column; an image analysis algorithm is introduced, and meanwhile, a correction coefficient is introduced, so that small pores among particles are precisely measured and calculated. The method is simple in process and convenient to operate, and the ore porosity can be rapidly and accurately measured and calculated.

The invention relates to a shale gas multi-layer stacked reservoir dessert evaluation and separate layer fracturing decision-making method, and belongs to the technical field of oil-gas field development, and the method comprises the following steps: calculating porosity, adsorbed gas content, organic carbon content and vitrinite reflectance geological parameters; calculating brittleness index and fracture toughness engineering parameters; carrying out normalization processing and weight distribution by adopting a CRITIC weight method, constructing a geological-engineering comprehensive evaluation model, and dividing four types of sweet spot areas; through a mixed method of main parameter priority and abnormal value verification, preferentially analyzing interlayer gradients of high-weight parameters, and performing local fine verification on abnormal abrupt change of low-weight parameters based on a 3 sigma principle; and in combination with a rigid-flexible layering model, a fracturing layering strategy is dynamically decided, and the complexity of the fracture network is optimized. According to the method, the reservoir identification precision is improved through multi-parameter fusion evaluation, the fracturing efficiency is enhanced through dynamic hierarchical decision, the development cost is finally reduced, and technical support is provided for efficient development of shale gas.

The invention provides a method for detecting the porosity of in-situ dump-leached ore, and belongs to the technical field of ore porosity detection. The method comprises the following steps: taking an ore body on site, preparing a filling agent, performing vacuum gradient impregnation, curing, dissolving and separating the ore to obtain a pore model; melting and softening the pore model to obtain the pore volume in the ore, and finally obtaining the porosity of the ore; wherein nano SiO2 is added into the filler resin, and permeation of tiny pores is guaranteed based on the size effect and the interface optimization effect of a nano material; and through vacuum gradient impregnation, sufficient permeation of tiny pores is further ensured. According to the detection method, the limitation of a drainage method on ore pores containing water-soluble minerals is avoided; the problems that ore samples need to be collected and taken back to a laboratory to be detected in a mercury injection method, a gas adsorption method and the like, the ore body structure is damaged, and consumed time is long are solved. Detection can be carried out on site, ore permeability parameters are provided for the in-situ dump leaching process, and a real-time basis is provided for flow selection.

The invention relates to a reservoir porosity and permeability prediction method based on a dynamic committee integration model, and the method comprises the following steps: obtaining an original data set which comprises shale content, porosity, permeability, GR, AC, CNL, DEN, RT, RXO, SP, CALC, CALI, depth and lithologic labels; performing data enhancement on the original data set; determining main control factors influencing the porosity and the permeability; constructing a dynamic committee integration model; inputting the main control factors into a dynamic committee integration model; and utilizing the trained dynamic committee integration model to respectively predict the porosity and the permeability. According to the method, the logging data is processed by adopting a machine learning method, reservoir parameters can be efficiently and accurately predicted, and favorable support can be provided for oil-gas exploration and development; the dynamic committee integrated model constructed by the invention can dynamically adjust the weight of each model according to different geological conditions and data features, can more flexibly adapt to the geological condition of a research area compared with a single model, and improves the prediction precision and generalization of the model.

The invention relates to the technical field of oil exploration, and discloses a rock mechanical parameter logging intelligent evaluation method considering continental facies shale fabric characteristics. The method comprises the following steps: 1, dividing shale lithofacies, identifying and determining the bedding development thickness of a sample through an optical microscope, determining a granularity experiment of the sample through granularity analysis, and forming mineral composition and bedding factor lithofacies division; 2, constructing a typical logging facies based on a rock electricity response relationship, and dividing the imaging logging facies of the research area according to natural gamma curves of conventional logging and imaging logging, core deposition structure characteristics and imaging logging image characteristics; 3, testing the longitudinal and transverse wave velocities and the porosity of different shale lithofacies, and calculating the dynamic elastic modulus and the Poisson's ratio. Through fine division and description, the method for accurately evaluating the bedding development condition, the particle size characteristics and the mineral components of the shale sample is provided, the testing time is short, the cost is low, and accurate evaluation of shale can be achieved.

The invention relates to the field of digital core modeling, in particular to a stomatal basalt three-dimensional digital core reconstruction method, which comprises the following steps: extracting target characteristic parameters from CT (Computed Tomography) scanning gray volume data of a stomatal basalt sample, the target characteristic parameters comprising porosity, cluster number, cluster size statistics, spatial uniformity index and simplified compactness; generating an initial three-dimensional digital core model according to the target characteristic parameters; carrying out iterative optimization on the initial three-dimensional digital core model by utilizing a self-adaptive Markov chain-Monte Carlo algorithm so as to enable cluster features of the optimized three-dimensional digital core model to approach target feature parameters; and performing curvature smoothing post-processing on the optimized three-dimensional digital core model to obtain the pore basalt three-dimensional digital core model. The model not only is highly matched with real pore basalt in the aspect of macroscopic statistical characteristics, but also shows natural and smooth curved surface characteristics in the aspect of microscopic pore boundary morphology, and provides a reliable digital basis for subsequent rock physical property analysis.

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 relates to a preparation method of a multi-porosity gradient multi-layer dry method thick electrode, which comprises the following steps of: stirring and mixing raw materials under the condition that the temperature is less than or equal to 18 DEG C to obtain a premix; quickly shearing the premix and raising the temperature to be less than or equal to 120 DEG C to obtain fiberized powder; the powder is subjected to one-time double-roller hot pressing to form a film continuously, the formed film continuously passes through double rollers and hot pressing to be thinned and then is rolled, multiple layers of films are obtained, and each layer of film has different porosity; the multiple layers of diaphragms with different porosities and the current collector are subjected to continuous double-sided lamination through double-roller hot pressing; the temperature of the double rollers is controlled to be smaller than or equal to 180 DEG C, the pressure of the double rollers is controlled to be 10-200 T, the laminating speed is controlled to be 1-10 m / min, the gap of the double rollers is controlled to be smaller than or equal to 1000 microns, and the multi-layer dry-method thick electrode which is composed of multiple layers of membranes is obtained after rolling, the compaction density of each layer of membrane is different, and the material proportion of each layer of membrane is the same; the method has the advantages that only the compaction density of the pole piece needs to be regulated and controlled, the material ratio does not need to be adjusted, and the interface property is more stable after multiple times of film forming.

The invention discloses a high-pressure die-casting part mechanical property prediction method, device, equipment and medium, and relates to the field of die-casting material performance prediction.The method comprises the steps that a finite element model data file and a porosity result data file are extracted based on a die flow simulation analysis result of a target die-casting part, constructing a finite element mesh model for receiving porosity data; performing porosity data mapping based on the extracted data file and the constructed finite element mesh model, and constructing a finite element simulation mesh model with porosity characteristics; establishing a representative volume element model and carrying out homogenization treatment to obtain a mechanical property parameter card with a porosity material; on the basis of the finite element simulation grid model and the mechanical property parameter card, finite element simulation analysis of the anisotropic mechanical behavior casting with the porosity characteristic is conducted on the target die-casting part to obtain the mechanical property, rapid response can be conducted on the mechanical property of the die-casting part, and the prediction efficiency of the mechanical property of the die-casting part is improved.

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 sampling device and method for detecting the oil content of oil sand ore based on hyperspectrum, and relates to the technical field of oil sand ore sampling. Comprising a mounting base, a drilling barrel and a drill bit, the mounting base, the drilling barrel and the drill bit are sequentially in threaded connection in the direction from top to bottom, the mounting base is rotationally connected with a connecting piece located in the drilling barrel, and the lower side of the connecting piece is in threaded connection with a middle barrel; a guide assembly for reducing the moving resistance of the oil sand sample is arranged on the connecting piece; the guide assembly comprises two inner half cylinders which are symmetrically distributed and a sample containing cylinder. An oil sand sample entering the space between the two inner half cylinders is gradually wrapped by the sample containing cylinder, and the oil sand sample is isolated from the inner half cylinders, so that the oil sand sample does not make direct contact with the inner half cylinders, friction resistance borne by the oil sand sample in the process of moving into the inner half cylinders is reduced, interference to the porosity of the oil sand sample and the rock structure is reduced, and the oil sand sample detection accuracy is improved. And the detection error of the oil content is reduced.

The invention provides a method for calibrating a rolling friction coefficient and surface energy in a process of simulating a ceramic membrane to filter lignin by DEM-CFD, develops a semi-analytical DEM-CFD coupling interface, and on the basis, takes movement and deposition behaviors of lignin particles in a process of filtering black liquor lignin by a single ceramic membrane pore as research objects, and provides a method for calibrating the rolling friction coefficient and the surface energy in the process of simulating the ceramic membrane to filter the black liquor lignin. The capturing mechanism of lignin particles in a ceramic membrane is researched, and it is determined that key factors influencing lignin particle deposition morphology, average coordination number, coordination number distribution and lignin particle deposition structure porosity are rolling friction coefficients and surface energy among lignin particles; the rolling friction coefficient and the surface energy between the lignin particles and the ceramic membrane have little influence on the ceramic membrane; the influence of the rolling friction coefficient among lignin particles and the surface energy parameters on the deposition morphology, coordination number and porosity of the lignin particles is comprehensively considered, calibration work of the rolling friction coefficient and the surface energy parameters is completed, and a powerful tool and accurate simulation parameters are provided for subsequent research of a membrane pollution mechanism.

The invention discloses a leakproofness evaluation method and device for XAI gas stored in an underground gas storage. The method comprises the following steps: acquiring set parameters obtained by performing an XAI air tightness test and a rock core mechanical test on a rock sample of a surrounding rock layer of the underground gas storage; the set parameters are substituted into a fluid-solid coupling model, and the leakage range of the gas storage after the set operation time is obtained through simulation; according to the leakage range, the cavity size, the temperature and the pressure of the gas storage and the thickness and the porosity of each stratum, the leakage volume is determined; according to the leakage range and the leakage volume, the sealing performance of the XAI gas stored in the gas storage is evaluated. According to the method, the leakage range of the high-pressure XAI gas in the surrounding rock stratum of the underground gas storage can be quantitatively predicted through an experiment and simulation calculation method, reasonable evaluation of the sealing performance of the gas storage is achieved, and the method has important guiding significance on design and construction of a gas storage project.

The invention relates to the technical field of surface engineering, discloses a method and a system for preparing a yttrium fluoride composite ceramic coating through atmospheric plasma spraying, and aims to solve the problems that the yttrium fluoride coating generates a brittle aluminum fluoride layer on the surface of an aluminum substrate due to fluorine ion diffusion, so that the bonding strength is low, and the yttrium fluoride coating is easy to peel off and lose efficacy. The method comprises the following steps: sequentially carrying out mechanical polishing, ultrasonic cleaning and sand blasting roughening on an aluminum base material; preparing composite powder formed by mixing yttrium fluoride, yttrium oxide and nanometer aluminum oxide according to the mass ratio of 75: 20: 5; sequentially depositing a yttrium oxide transition layer, a composite barrier layer and a yttrium fluoride functional layer by adopting atmospheric plasma spraying; and finally carrying out vacuum post-heat treatment. According to the system, through the synergistic effect of the three layers of gradient structures, the transition layer forms an yttrium aluminate metallurgical bonding interface, the barrier layer inhibits fluorine ion diffusion through the dual mechanisms of nanometer aluminum oxide physical barrier and yttrium oxide chemical adsorption, and the functional layer guarantees the plasma erosion resistance. According to the method, the bonding strength of the coating is remarkably improved to 50 MPa or above, the porosity is lower than 3%, stripping is avoided under 500-hour high-energy plasma bombardment, the process cost is reduced by 40%, and the method is suitable for cavity protection of semiconductor etching equipment.

The invention discloses environment-friendly ceramic and a preparation method thereof, and belongs to the technical field of ceramic. The environment-friendly ceramic comprises the following raw materials in parts by weight: 30-50 parts of red mud, 20-40 parts of fly ash, 10-25 parts of purple clay, 5-15 parts of quartz, 3-8 parts of a composite binder, 3-8 parts of a composite fluxing agent and 40-50 parts of deionized water. The environment-friendly ceramic prepared by the method disclosed by the invention has excellent breaking strength and apparent porosity.

The invention provides a layout method and system for a carbon dioxide injection well and a pressure control well. The method comprises the steps that the reservoir thickness, porosity, permeability and formation pressure distribution data of a target reservoir are obtained; determining a deployment area according to the reservoir thickness, the porosity, the permeability and the formation pressure distribution data; based on a preset carbon dioxide injection well distance, carbon dioxide injection wells are arranged in the deployment area in an equilateral triangle grid mode; and a pressure control well is laid based on the center of the equilateral triangle grid, and the layout of the carbon dioxide injection well and the pressure control well is obtained. According to the technical scheme, the carbon dioxide injection well and the pressure control well are reasonably arranged, and the safety and economical efficiency of carbon dioxide geological sequestration are improved.

The invention provides a tap density prediction modeling method for a prebaked anode formula, which comprises the following steps: screening calcined coke into multiple particle sizes, measuring the tap density of each particle size, and obtaining physical property parameters; performing high-precision three-dimensional scanning on the representative particles, and constructing a digital model retaining geometric features such as edges and corners and textures; then, by adjusting the particle surface friction coefficient and the corner angle parameter, the deviation between the simulated porosity and an experimental value is within a preset range, and the feasibility of the model is verified; then, performing three-dimensional equal-proportion scaling on the reference fraction model, correcting physical parameters, and then, importing the reference fraction model into a discrete element system to simulate multi-fraction accumulation; and finally, based on the real-time porosity data, establishing a tap density prediction model of the irregular particle system by utilizing the negative correlation between the porosity and the tap density, and realizing the accurate prediction of the tap density under different proportions. The prediction accuracy of the tap density of the prebaked anode can be improved.

The invention discloses a method and equipment for evaluating the sensitivity of shale porosity to the pH value of a well-entering fluid, and the method comprises the steps: obtaining a plurality of groups of experimental data of a shale sample, the experimental data comprising a first initial porosity and a first pH value of the well-entering fluid corresponding to the shale sample; a three-dimensional response curved surface model of the porosity increment, the initial porosity and the pH value is constructed according to the experimental data, the initial porosity comprises the first initial porosity, and the pH value comprises the first pH value; according to the three-dimensional response curved surface model, the target initial porosity of the target shale and the target pH value of the well entering fluid corresponding to the target shale, the target porosity increment of the target shale is predicted; and evaluating the sensitivity of the target shale to the second pH value according to the target porosity increment. According to the technical scheme provided by the invention, the porosity increment of the target shale can be predicted, and the sensitivity of well entering fluids with different pH values can be quantitatively evaluated based on the porosity increment.

The invention discloses a high-content fly ash unfired water permeable brick and a preparation method thereof, and relates to the technical field of building materials, the unfired water permeable brick comprises the following raw materials by weight: 55-75 parts of fly ash, 20-35 parts of aggregate, 8-15 parts of a composite cementing material, 0.5-2 parts of toughening fiber, 1-3 parts of a pore regulator, 0.1-0.5 part of a surfactant, and 10-18 parts of water. Wherein the aggregate is 1-3mm waste ceramic particles and 2-5mm building recycled aggregate (1: 1-1: 2); and the rest components have specific proportions, specifications and modification requirements. According to the invention, 55-75 parts of F-type II-grade or higher high-content fly ash is matched with the waste ceramic particles and the pretreated building recycled aggregate to serve as the composite aggregate, solid waste is digested, natural resource dependence is reduced, and the method conforms to the direction of green building materials; by means of a modified composite fiber and composite gel system, the pressure resistance is larger than or equal to 25 MPa, the fracture resistance is larger than or equal to 3.5 MPa, the water permeability is larger than or equal to 1.5 * 10 cm / s, the freeze-thaw loss is smaller than or equal to 3.5%, and the grinding pit is smaller than or equal to 30 mm; through graded mixing, bidirectional pressurization and segmented maintenance, the porosity is kept to be 15%-25%, the percent of pass is increased, and the cost is reduced.

The invention belongs to the technical field of metal processing, and discloses an automatic welding method and system.The method comprises the steps that a functional gradient transition layer is arranged between a steel workpiece and a copper workpiece, components of the transition layer change in a gradient mode from a Ni-rich layer to a Ni-Cu solid solution layer in the thickness direction, and the transition layer is doped with nano reinforced particles or rare earth elements; the metallurgical compatibility is fundamentally improved; in the welding process, a composite energy source and an auxiliary physical field are adopted, and a sensor system is used for monitoring multi-modal data in real time; monitoring data are input into a predictive control model based on artificial intelligence, the model can predict the defect trend of a future moment, and welding parameters are actively adjusted in a feedforward mode. According to the method, the problems that in a traditional method, intermetallic compounds are difficult to inhibit, and process control lags are solved, and a welding joint which is free of cracks and pores, extremely thin in IMC layer and excellent in mechanical property can be obtained.

The present invention belongs to the field of characterization of building material properties, and particularly relates to a sliding evaluation method for the microstructure of the interfacial transition zone of building materials. It includes: processing the test specimen to be tested; performing tomographic scanning imaging once respectively under the original and imaging states of the specimen with the same parameters; using image registration technology to spatially match the results of the two scans; obtaining the local porosity inside the specimen according to the change in gray values at the same positions in the results of the two scans; using sub-regions with different widths to slide across the interfacial transition zone of concrete. As the width of the sub-region increases, the peak of the one-dimensional porosity distribution curve gradually disappears; the width of the sub-region when the peak of the curve is about to disappear is taken as the width of the interfacial transition zone, and at this time, the peak is the width of the interfacial transition zone. The present invention adopts the method of moving average, which can overcome the problems of irregular shape and insufficient CT resolution of the interfacial transition zone and characterize the microstructure of the interfacial transition zone of concrete.

Methods, devices and systems are described for improving the manufacturing of components, devices, and materials. The described technique can predict microstructures including defects, such as cracks, porosity, undesirable phases, or non-optimal grain structures, segregations, and dendrite structures of alloys and superalloys manufactured by additive manufacturing, such as three-dimensional (3D) printing including laser powder bed fusion (LPBF) and laser melting deposition (LMD). Example alloys and superalloys include Rene 65, IN625, Mar-M-247, CM247LC, Rene 108, IN738, Ti-6Al-4V, AlSi10Mg, or IN718.

The invention discloses a coarse and fine particle inclusion photoelastic sample preparation and contact force chain network analysis method, and relates to the field of particle material mechanical tests, and the method comprises the following steps: calculating target void ratios under different coarse particle contents through a discrete element method; generating a corresponding numerical calculation sample according to the target void ratio and the preset coarse grain content, and exporting related parameters to prepare a coarse and fine grain inclusion photoelastic sample with the same initial compactness; in combination with non-polarization and polarized photoelastic images of the photoelastic sample, extracting intergranular contact force and constructing a contact force network; based on the contact force network, a strong contact force network is extracted and microscomic analysis is carried out, and force transmission characteristic comparison results under different coarse grain content conditions are obtained. According to the method, preparation efficiency of samples with different coarse grain contents and controllability of an initial state are realized, an influence rule of the coarse grain contents on a force chain structure and force transmission characteristics thereof is disclosed based on analysis of a contact force chain network, and the method is of great significance in researching a force transmission mechanism of a coarse and fine grain inclusion system and microstructure characteristics thereof.

The invention relates to the technical field of rock mass mechanical monitoring, and particularly discloses a stope roof fracture intelligent early warning system based on hard rock damage evolution, which comprises a rock-soil physical property detection module for obtaining rock-soil physical property parameters of rock-soil below a stope roof based on measurement while drilling in the drilling process, and sending the rock-soil physical property parameters to the early warning module; the parameters comprise stress response, wave velocity change, fracture evolution and porosity of rock and soil; the drilling data acquisition module is used for acquiring drilling process data including bit pressure, torque and drilling speed in the drilling process, and the drilling data acquisition module comprises a sensor array connected with drilling equipment; and the data processing module is used for obtaining an analysis result associated with the damage state of the stope roof by combining a hard rock damage evolution algorithm according to the rock-soil physical property parameters and the drilling process data, and determining the fracture risk of the stope roof according to the analysis result. The early warning of the fracture risk of the stope roof is realized by integrating rock-soil physical properties and drilling data in real time and applying a hard rock damage evolution algorithm.

The invention relates to the field of environmental analysis, and provides a gas hydrate three-phase coexistence region modeling method and system. The method comprises the following steps: acquiring three-dimensional seismic data and original logging data, and preprocessing the original logging data to obtain corrected logging data; respectively performing logging interpretation and structural interpretation according to the corrected logging data and the three-dimensional seismic data to obtain interpretation results; converting a structure interpretation result in the interpretation results into a three-dimensional geological structure model, and performing grid division on the research area by adopting a corner grid system to obtain a structure grid model; and performing attribute modeling on the constructed grid model based on variation function analysis, and predicting porosity and saturation in a three-dimensional space through a Kriging interpolation algorithm to obtain a three-phase coexistence region model. According to the method, the influence of complex geological conditions on the accuracy of the model is reduced, and the precision of the three-phase coexistence region model is remarkably improved.

A rock porosity prediction method based on small-sample while-drilling data inversion is provided, including: collecting while-drilling parameters during laboratory or field drilling processes, including parameters such as torque M, propulsion F, rotational speed N, drilling speed V, drill pipe amplitude A, and vibration acceleration a; inputting the different types of multi-stage denoised while-drilling parameter data into a backpropagation regression-genetic algorithm (BP-GA) model for discrete point elimination, data augmentation, and iterative calculation to form a new while-drilling dataset; inputting the time-frequency domain feature graphs of while-drilling parameters for rocks with different porosities into a rock porosity convolutional neural network (VG-CNN) prediction model, obtaining an inversion model between the time-frequency domain features of while-drilling parameters and porosity through training and learning, ultimately achieving inversion prediction of rock porosity according to real-time field while-drilling parameters.

Provided in the embodiments of the present description is a method for determining a reasonable soaking time for a shale oil / gas well. The determination method uses a shale oil / gas well reasonable soaking time testing apparatus to carry out testing experiments, and comprises: selecting a rock core sample; measuring to obtain the porosity of the rock core sample; simulating the in-reservoir state of a first rock core sample; injecting methane and reaching the reservoir pore pressure; injecting a simulated fracturing fluid into a rock core clamping holder; measuring the resistivity change curve of the first rock core sample; measuring and obtaining the pressure attenuation half-life of a first pressure sensor until an inflection point appears on the resistivity change curve of the first rock core sample; obtaining the experiment durations under which the first rock core sample and a second rock core sample have the shortest pressure attenuation half-life; obtaining the time points at which the first rock core sample and the second rock core sample exhibit maximum rock core nuclear magnetic signal change amplitude; after sorting, obtaining a soaking time at the rock core scale; and obtaining a soaking time for the shale oil / gas well.