2078 results about "Rock core" patented technology

The invention discloses a reef limestone strength prediction method based on pore feature machine learning, and belongs to the technical field of crossing of geotechnical engineering and artificial intelligence, and the method comprises the steps: obtaining a reef limestone three-dimensional digital core, and extracting pore features after preprocessing; establishing a multi-source pore-physical coupling feature data fusion framework based on the pore features; integrating a random forest and a gradient boosting tree, dynamically weighting parameters in the pore features, designing a composite loss function, performing hierarchical training, and embedding physical constraints to construct a reef limestone strength prediction machine learning model; layered K-fold cross validation is adopted, the robustness of the reef limestone strength prediction machine learning model is tested, and interpretability is verified. According to the method, through data processing, the reef limestone strength prediction machine learning model is constructed and optimized, the relationship between the pore characteristics and the reef limestone strength is deeply excavated, the model prediction accuracy, reliability and interpretability are improved, and an effective solution is provided for reef limestone strength prediction.

The invention relates to the technical field of reservoir evaluation, in particular to a deep learning-based reservoir evaluation model construction method and system. The method comprises the following steps: acquiring logging data and core analysis data of a target work area, and respectively carrying out abnormal value processing and missing value filling to obtain complete logging data and complete core data; calculating reservoir pore structure parameters based on the complete core data; and inverting the macroscopic porosity of the reservoir based on the complete logging data. According to the method, the accuracy, the stability and the robustness of reservoir evaluation are remarkably improved by fusing the core data and the logging data and combining the pore structure parameters, the macroscopic porosity and the geological attention mechanism, the problems of data missing and small samples are solved, and the interpretability and the geological consistency of the model are enhanced.

The invention provides an accurate transportation control method and system for a remediation agent for in-situ remediation of underground water, and relates to the technical field of underground water pollution remediation, and the method comprises the steps: collecting an image of a drilling rock core of a polluted site and sensor data, extracting features through a visual attention network and a time sequence attention network, and carrying out the feature extraction; the features are input into a conditional probability diffusion model and fused to generate hydrogeologic feature vectors, a high-precision hydrogeologic feature model is constructed in combination with space attention mapping and a generative diffusion model, the high-precision hydrogeologic feature model is input into a multi-region collaborative simulation environment for optimization calculation, and monitoring data are fused through edge calculation nodes. And training the lightweight control model to execute repair medicament transport control.

The invention discloses a reservoir parameter prediction method based on an improved Transform. The reservoir parameter prediction method comprises the following steps: collecting logging data and corresponding rock core lithologic data; preprocessing the logging data through a rock core homing method; selecting an input logging characteristic curve through Pearson linear correlation analysis, and carrying out standardization processing on the input logging characteristic curve; the method comprises the following steps of: improving a Transform by using a TransBlock multi-head attention mechanism and a Real-LN residual structure, and performing feature extraction on a training sample by using a TransBlock algorithm obtained after improvement to form a TransBlock feature model; and obtaining a reservoir parameter prediction result from the to-be-detected data through the TransBlock feature model. According to the method, the problems of less data and insufficient generalization ability in reservoir parameter prediction are solved by improving Transformer, the prediction performance and generalization adaptation ability of the model are improved through limited logging data and hidden correlation between the logging data, and the accuracy of reservoir parameter prediction is remarkably improved.

The invention provides a core permeability prediction method based on time sequence decomposition and multi-dimensional feature interaction, and belongs to the technical field of core permeability prediction. The method comprises the following steps: performing CT scanning on multiple sandstone samples to obtain high-resolution three-dimensional rock core original data; the method comprises the following steps: performing image preprocessing on original data, performing adaptive threshold segmentation, and dividing pores and matrixes to obtain three-dimensional binary image data; performing small window sliding sampling on the binary image data to obtain three-dimensional core small cubes, and calculating the permeability of each core cube as a label to obtain a final experimental data set; a core image sequence is decomposed from an experimental data set, fusion features are extracted, information is extracted from the fusion features from three dimensions of channel, height and width, features of different dimensions are combined, and the permeability is predicted through a full connection layer. According to the method, lightweight design is adopted, multi-dimensional information of the core image is fully utilized, prediction efficiency and precision are remarkably improved, and computing resource consumption is reduced.

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 relates to the technical field of geological data processing, in particular to a prospecting method and system based on geological text data, and the method comprises the following steps: obtaining exploration drilling data, extracting lithologic description, mineral composition and fracture filling conditions of a rock core, calculating the correlation index of drilling depth and mineralization thickness, screening the change trend of ore grade, and obtaining the geological text data. And obtaining mining area borehole lithology distribution data. According to the method, by deeply analyzing drilling data and lithologic characteristics, accurately recognizing a mineralized fluid accumulation area and improving the prediction precision of an ore body occurrence position, boundary control of a mineralized zone is optimized through measurement and calculation of spatial homogeneity, the extension precision of the mineralized zone is enhanced, scientific adjustment of a prospecting range is ensured through detailed analysis of altered mineral characteristics, and the method is suitable for large-scale popularization and application. The reasonability of demarcation of the prospecting target area is improved, multi-dimensional data and parameter analysis are integrated, a complete prospecting logic chain is constructed, and mineral product prediction precision and resource exploration efficiency are remarkably improved.

The invention provides a construction method of a compact sandstone core natural fracture characteristic parameter characterization model. The construction method specifically comprises the following steps: step 100, self-making a compact sandstone core natural fracture image data set; step 200, establishing an efficient self-adaptive Frac-YOLOv8 rock core natural fracture segmentation network; step 300, training the network by using the data set to obtain a rock core natural fracture segmentation model; step 400, inputting a core image to be segmented into the model, and segmenting cracks; step 500, generating a binary crack data set; step 600, applying a crack skeletonization extraction algorithm to quantify the crack length; step 700, using a pixel statistical algorithm to quantify the actual area of the crack and calculating the average width of the crack; and finally, calculating the maximum width pixel value of the crack by using a crack variable circle algorithm, and quantifying the maximum width through the diameter of an inscribed circle. The method provides an effective basis for evaluation and analysis of natural rock core cracks of tight sandstone.

The invention relates to the technical field of crustal stress measurement, in particular to a rock core three-dimensional crustal stress measurement system and method based on multi-source data. The method comprises the following steps: acquiring well wall damage information, and identifying a damage type in the well wall damage information; multiple sections of core samples are selected based on the well wall damage information, and overburden pressure parameters are determined based on the multiple sections of core samples; a rock mechanics experiment is conducted on the rock core sample, rock mechanics parameters are generated, and the rock mechanics parameters comprise the Poisson's ratio, the Young modulus, the uniaxial compressive strength value and the tensile strength value. According to the method, the difference value of the maximum stress and the minimum stress is combined with the multi-source coupling constraint principal stress interval, the intersection points are identified through the topological visualization graph, the numerical range of the maximum principal stress and the minimum principal stress can be accurately determined, and then the information of the three-dimensional crustal stress is obtained.

The invention provides a core photograph automatic correction and cutting method based on YOLOv8-seg and perspective transformation, and belongs to the technical field of image processing, and the method comprises the steps: S1, collecting a core box image; s2, polygonal point sequence labeling; s3, obtaining an optimal segmentation model; and S4, cutting. Aiming at the problems of much manual intervention, low efficiency and poor adaptability in the existing core photograph processing, the invention provides a set of full-automatic end-to-end solution without preprocessing, realizes rapid and stable correction and cutting of the core photograph in a complex scene, relieves the mechanical labor burden of geological personnel, focuses on the core exploration work, and improves the core exploration efficiency. And the practical application requirements of large-scale geological exploration projects are met.

The invention relates to a coal mine roof rock stratum fracture inversion modeling method based on a drill hole group, which comprises the following steps: collecting drill hole group rock core data of a coal mine roof sampling area, and calculating the fracture linear density of each drill hole; acquiring a conversion factor, and converting the fracture linear density value into a local fracture volume density value based on the conversion factor; performing spatial interpolation on the local fracture volume density value to generate a three-dimensional space global fracture volume density intensity field of the coal mine roof rock stratum; taking the three-dimensional space global fracture volume density intensity field as a three-dimensional space fracture density constraint, and generating a coal mine roof rock discrete fracture network model database; and screening the coal mine roof rock stratum discrete fracture network model meeting the preset precision from the coal mine roof rock stratum discrete fracture network model database as final output, and completing coal mine roof rock stratum inversion modeling. The method is high in modeling precision, water inrush early warning and support design can be directly guided, and the safety of the coal mine roof is remarkably improved.

The invention provides a bedding structure rock finite element model reconstruction method based on a rock core digital image, and belongs to the technical field of digital rock cores, and the method comprises the following steps: S1, obtaining a gray level image of a rock sample; s2, calculating a transverse mean value of the analysis area to obtain a longitudinal gray level distribution curve; s3, applying moving average filtering and smooth filtering to obtain a smooth gray curve; s4, first-order difference is carried out on the smooth gray level curve, the position with the absolute difference value larger than a threshold value is taken as an initial stripe boundary candidate, and starting and stopping pixels, the width and the average gray level of each stripe are determined; s5, mapping each clustering label interval into a physical interval based on FEM grid partition; and S6, constructing a sample finite element model, and generating a reconstructed finite element model with real partition information. According to the method, the macrostructure features of the rock can be efficiently recognized and extracted by directly utilizing the pictures and combining gray profile analysis, and efficient and low-cost reconstruction of the finite element model of the bedding structure rock is achieved.

The invention relates to an integrated experimental system and method for simulating sand production-sand prevention of an argillaceous silt hydrate reservoir, and the system comprises a reservoir sand production / mechanical property evaluation simulation device which comprises a heat exchange chamber and a high-pressure reaction kettle arranged in the heat exchange chamber, and the high-pressure reaction kettle is internally provided with a core holder; the gas displacement and control device is connected with the reservoir sand production / mechanical property evaluation simulation device; the confining pressure loading device is connected with the high-permeability confining pressure layer in the reservoir sand production / mechanical property evaluation simulation device and is used for simulating the confining pressure environment outside the reservoir core sample; the liquid displacement and control device is connected with the high-pressure reaction kettle and is used for providing liquid displacement conditions for the reservoir core sample; the gas-liquid-solid separation device is connected with the high-pressure reaction kettle and is used for separating a gas-liquid-solid mixture; the visual sand prevention simulation device for the screen pipe unit comprises a visual acrylic pipe, and the data acquisition device is used for acquiring and processing data of the sand prevention experiment of the visual acrylic pipe.

The invention belongs to the technical field of carbonate rock oil-gas exploration, and particularly discloses a complex carbonate rock logging lithology identification method based on a diffusion model, and the method comprises the following steps: determining the lithology types of a plurality of observation wells based on the on-site rock core observation and slice analysis, and synchronously obtaining the logging data of the corresponding observation wells, constructing a lithology training data set in combination with a depth corresponding relationship between the lithology category and the logging data; a diffusion model is adopted to generate and supplement lithology categories with insufficient samples; logging data is adopted as an input feature, the lithology category is adopted as an output label, and a convolutional neural network fused with a Bayesian optimization algorithm and a Transformer hybrid model are utilized to train a lithology identification model; and inputting to-be-identified logging data into the trained lithology identification model, and outputting a lithology identification result. According to the method, high-precision lithology identification can be realized under the conditions of deep layers and complex stratums.

The invention discloses a shale hydrocarbon expulsion efficiency evaluation method, device, equipment and medium, and relates to the technical field of shale gas exploration and developing.The shale hydrocarbon expulsion efficiency evaluation method includes the steps that hydrocarbon generation quantity charts of shale under different total organic carbon TOC grades are constructed; testing the water saturation of the core sample of the shale to obtain the water saturation rate of the core sample; carrying out oil washing treatment on the rock core sample, and carrying out porosity test to obtain rock porosity and rock apparent density; obtaining a crude oil sample of a production layer where the core sample is located, and performing a crude oil physical property experiment to obtain crude oil density; when key parameters such as water saturation rate, rock porosity, rock apparent density, crude oil density and shale hydrocarbon generation quantity charts under different total organic carbon TOC grades of a core sample and key parameters of hydrocarbon release and migration in a hydrocarbon expulsion process are measured, a dynamic measurement process is adopted, and the dynamic measurement process can realize dynamic simulation of a hydrocarbon migration path; and finally, accurate evaluation of the shale hydrocarbon expulsion efficiency is realized.

The invention provides a method and device for predicting the pile-forming strength of a high-pressure jet grouting pile, and relates to the technical field of pile-forming strength prediction.The method comprises the steps that construction parameters are collected in real time through a multi-source sensor, a construction parameter matrix is constructed, and the soil layer boundary depth and the dielectric constant are obtained through geological radar scanning so as to distribute static weight factors; and calculating an intensity correction coefficient based on geological parameters of the core sample, finally establishing an intensity prediction model fusing the dynamic weight, the slurry solidification time, the temperature influence factor and the geological correction coefficient, and setting a three-level early warning mechanism. The core innovation of the scheme is that construction parameters and geological characteristics are dynamically coupled, the model precision is remarkably improved through double calibration of geological radar and core data, and the problem that a traditional method is insufficient in adaptability to soil layer difference is solved through introduction of a geological correction coefficient; and the real-time closed-loop optimization of the construction process is realized through the multi-sensor time synchronization application.

The invention belongs to the technical field of geological research, and discloses a multi-scale lithofacies identification and sedimentary microfacies reconstruction method and system, and the method comprises the steps: data collection and preprocessing: collecting core data, slice data, logging data and seismic data of a research region; multi-scale lithofacies feature extraction, wherein the multi-scale lithofacies feature extraction comprises micro-scale feature extraction, medium-scale feature extraction and macro-scale feature extraction; constructing and training a multi-scale lithofacies recognition model, constructing the multi-scale lithofacies recognition model by adopting a deep learning algorithm, and taking the extracted microscopic, mesoscopic and macroscopic lithofacies features as input data; multi-scale lithofacies identification: extracting multi-scale lithofacies features from the preprocessed multi-source data; and sedimentary microfacies reconstruction: establishing a lithofacies-sedimentary microfacies conversion relation in combination with a sedimentology principle and a geological knowledge base. According to the method, multi-source data such as rock cores, slices, logging and earthquakes are integrated, lithofacies features are extracted from microcosmic, mesoscopic and macroscopic multiple scales, the features and changes of lithofacies can be comprehensively and accurately reflected, and compared with a traditional single-scale method, the accuracy and reliability of lithofacies recognition are improved.

Systems and methods include obtaining well log data and core sample data of a subsurface formation; generating, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation; using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data; forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters; training a supervised machine learning model using the training dataset. While drilling a well in the subsurface formation, logging-while-drilling data is obtained from drilling equipment used to drill the well; and rock types in the subsurface formation are determined using the supervised machine learning model and the logging-while-drilling data.

The invention relates to the technical field of oil field reservoir research, in particular to a fracturing fracture imaging physical simulation method and system combined with non-uniform contact electrodes, and the method comprises the steps: carrying out the resistance modal detection at the current time step through a low-frequency excitation signal in the fracture propagation simulation process, and obtaining a corresponding electric signal; obtaining a conductive fluid distribution result of the rock core fracturing test piece at the current time step by using the conductive fluid distribution inversion model; and fitting and comparing the electric field distribution result of the current time step with the conductive fluid distribution result of the current time step, and outputting the fracture parameters and the conductive fluid distribution result of the rock core fracturing test piece at the current time step based on the fitting and comparison result. According to the method, the electrical property change caused by the natural fracture and the fluid contained in the natural fracture is directly sensed in situ, the electrical signal is acquired, the response delay is reduced, the conductive fluid distribution inversion model is constructed by combining prior knowledge and deep learning, and the precision and reliability of conductive fluid distribution inversion are effectively improved.

The invention relates to a method, a device and equipment for analyzing in-situ formation fracture porosity of mixed rock. Comprising the steps of obtaining original data of a plurality of layers in a target block; taking out a first rock core sample from each layer of the target block, and performing experimental analysis on the first rock core sample of each layer to determine a target layer; taking out a second core sample from the target horizon, and performing a triaxial compression test on the second core sample to obtain a stress index; according to the original data and the stress index, the stratum fracture porosity of the target layer is calculated, the stratum fracture porosity is used for guiding workers to evaluate the real underground in-situ fracture porosity of the reservoir, and therefore an oil and gas reservoir exploitation plan of the target layer is made. The stratum fracture porosity calculated by the method represents the fracture porosity of the target horizon in the target block and represents in-situ characteristics in the stratum, so that the accuracy of reservoir evaluation is improved. A worker can formulate an oil and gas reservoir exploitation plan according to the formation fracture porosity, and the oil and gas reservoir yield is improved.

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 discloses a hydrate-containing porous medium resistivity model construction method considering hydrate heterogeneous distribution, and the method comprises the following steps: 1) manufacturing a rock core according to the rock composition of a target hydrate reservoir, and scanning the pore throat structure of the rock core through CT; 2) generating a hydrate in the core, and scanning the occurrence state of the hydrate through CT (Computed Tomography); 3) establishing a digital core model based on CT scanning data, and applying an electrostatic field; 4) changing hydrate saturation parameters in the model, and calculating the resistivity of the model through numerical simulation; 5) calculating the critical water saturation of the hydrate; and (6) fitting by taking the critical water saturation as a boundary to obtain a resistivity segmentation calculation model. According to the method, a hydrate-containing porous medium resistivity model is established based on a real rock core size, and an electric field simulation means is utilized to calculate a hydrate-containing porous medium resistivity value. In combination with CT scanning data, an undetermined coefficient of a porous medium resistivity calculation formula in a hydrate thermal decomposition experiment is obtained through electric field simulation, and a basis is provided for hydrate reservoir electric heating development.

The invention relates to the technical field of digital core modeling and artificial intelligence modeling, in particular to a weathered sandstone microscopic image generation method based on a conditional generative adversarial network, which comprises the following steps: acquiring and analyzing sandstone samples with different weathering degrees to construct a weathered sandstone multi-scale database; performing weathering geological knowledge extraction on the weathered sandstone multi-scale database to construct a sandstone feature tag set; and training the conditional generative adversarial network by using the sandstone feature tag set to obtain a weathered sandstone microscopic image generation model, and generating a weathered sandstone microstructure image with physical consistency by using the weathered sandstone microscopic image generation model. Therefore, the problems that in existing weathered sandstone physical and mechanical property modeling, the depth incidence relation between the microstructure and the macro-mechanical property is difficult to reveal, and a traditional modeling method is difficult to effectively fuse the advantages of a weathering geological mechanism and modeling generation, so that a reconstruction result lacks physical consistency and weathering response rationality are solved.

The invention provides a rotary impact type ultrasonic sampling device for deep rocks, which relates to the technical field of drilling and coring and comprises an anchoring unit, a feeding unit, a rotary unit, an impact unit and a coring unit which are sequentially connected from top to bottom. The device has the beneficial effects that the sampling device is firstly placed in a rock hole, and each anchoring tooth is fixed in the rock hole; then an ultrasonic transducer of the impacting unit and a motor of the rotating unit drive a drilling barrel to rotationally impact the rock, an electromagnet of the feeding unit drives a hydraulic cylinder to push the drilling barrel to drill downwards, the rock core enters the drilling barrel, and after the rock core in the drilling barrel reaches the needed length, the rock core pushes a push plate to move upwards to drive the lower end of a limiting rod to release a sliding block; the sliding block drives the core cutting rope to contract inwards to be in attached contact with a rock core, the core cutting rope cuts off the lower end of the rock core along with rotation of the drilling barrel, and therefore automatic drilling and coring can be achieved, coring operation is completed, the integrity of a rock core sample is guaranteed, and the core cutting device is particularly suitable for coring of deep hard rock.

The invention provides a shale oil reservoir fracturing process monitoring method, device and system, and belongs to the field of shale oil development. The method comprises the steps that original stratum physical property parameters and stress parameters of a target reservoir are obtained, and an initial reservoir model is constructed through numerical simulation; performing CT scanning and core pore three-dimensional reconstruction on the core obtained by the first coring to obtain an initial core pore network model; applying the monitored fracturing construction process to the initial reservoir model to obtain a dynamic construction reservoir model; after fracturing construction is finished, CT scanning and three-dimensional reconstruction of core pores are carried out on the core obtained through second-time coring, and a fractured core pore network model is obtained; through the model, the process of fracturing transformation on the target reservoir is visually displayed. And the shale reservoir fracturing crack propagation characteristic rule is obtained on the spatial scale, the influence of the production process on the fracturing crack is monitored for a long time on the time scale, and the production dynamic rule is predicted more accurately.

The invention discloses a low-permeability rock breakthrough pressure testing method based on schlieren imaging. The method comprises the following steps: putting a test rock sample into a confining pressure cavity of a rock sample holder; the injection end of the rock sample holder is connected with the fluid injection module, the outlet end of the rock sample holder is connected with the schlieren cavity, and the confining pressure cavity is connected with the confining pressure injection module; vacuumizing a pipeline of the testing device, injecting a degassed purified water medium into the schlieren cavity, and then carrying out optical path debugging until a clear schlieren image is acquired; loading confining pressure stress on the test rock sample, injecting water until the test rock sample is saturated, injecting carbon dioxide, observing a schlieren imaging system, recording a schlieren image of carbon dioxide breakthrough by a high-precision camera when the carbon dioxide breaks through the saturated rock sample, recording the breakthrough moment of the carbon dioxide, and recording the upstream and downstream pressure difference value at the moment; the breakthrough pressure is the breakthrough pressure of the carbon dioxide of the tested rock core sample. According to the invention, a trace concentration of carbon dioxide can be visually observed to break through the rock sample, so that the breakthrough pressure of the low-permeability rock can be accurately detected.

The invention discloses reservoir underground pore connectivity transformation simulation equipment, a reservoir underground pore connectivity transformation simulation system and an evaluation method. The equipment comprises a rock core transformation clamping device, a first magnet, a second magnet, a nuclear magnetic resonance signal controller, a storage and collection tank and a fluid displacement device, wherein a magnetic induction coil is arranged in the rock core transformation clamping device, and the first magnet and the second magnet are oppositely arranged on the two sides of the rock core transformation clamping device; the nuclear magnetic resonance signal controller is electrically connected with the first magnet, the second magnet and the magnetic induction coil respectively and is used for controlling the magnetic field intensity formed by the first magnet, the second magnet and the magnetic induction coil and measuring a nuclear magnetic signal of a rock core sample in the rock core transformation clamping device; the rock core modification clamping device comprises a fluid inlet, the fluid inlet is communicated with the storage and collection tank, and the fluid displacement device is connected with the storage and collection tank, so that fluid is displaced into the rock core modification clamping device under the action of the fluid displacement device, and modification and evaluation on the pore connectivity of the rock core sample under different fluid pressures are realized.

According to the fine fracturing geological modeling method based on the scratch test provided by the embodiment of the invention, the scratch test data representing the mechanical characteristics of a rock core sample material are accurately obtained by carrying out the scratch test on the rock core sample, the mechanical data are calculated and generated according to the scratch test data, and the wavelet transform is carried out on the mechanical data to obtain the fine fracturing geological modeling result. Compared with the prior art, the method has the advantages that the mechanical parameters of the reservoir rock are accurately obtained through scratch testing, the mechanical characteristics of the rock are represented, the mechanical characteristic model is optimized, and design data with higher accuracy is provided for a geological model.