22results about How to "Lower Exploration Costs" patented technology

The invention provides a sedimentary body lithology identification system and method for oil-gas exploration, and the system comprises a data collection module which is used for collecting seismic data and logging data; the data processing module is used for preprocessing the seismic data and the logging data; the data fusion module is used for fusing the preprocessed seismic data, logging data and a geologic model to generate a comprehensive data set; the lithology identification module is used for analyzing the comprehensive data set by using a machine learning algorithm and identifying the lithology type of the sedimentary body; the result verification module is used for performing comparison verification on the identification result and actual core analysis data; and the display module is used for visualizing the recognition result. The system provided by the invention not only can identify the lithology of the sedimentary body, but also can generate a visual three-dimensional lithology distribution diagram through three-dimensional geological modeling and a visualization technology. The visualization results enable geologists and engineers to clearly understand the underground structure and lithologic distribution so as to make more accurate exploration decisions and development plans.

The invention discloses a determination method for different origins of metallogenic fluids in a mountain-building type gold deposit in the south of Jiangsan. According to the method, through innovative sample collection and pretreatment, high-resolution sampling and fine treatment are carried out on specific parts of an ore deposit; advanced fluid inclusion comprehensive analysis is adopted, and inclusion information is accurately obtained in combination with multiple technologies; high-precision stable isotope determination and innovative interpretation in combination with a regional database are applied; element geochemical deep analysis and innovation index application are carried out, and SR-XRF and a machine learning algorithm are introduced; and finally, through comprehensive data fusion and intelligent analysis, constructing an intelligent platform and drawing a three-dimensional evolution model. The method can effectively integrate multi-source data, accurately determine the origin of the ore-forming fluid, overcome the limitation of a traditional method, improve accuracy and reliability, provide powerful support for deep exploration of ore deposit causes and prospecting exploration, and have important theoretical and practical significance.

The invention discloses a gold mine rapid prospecting method suitable for a plateau desert region, and belongs to the field of gold mine prospecting, and the method comprises the steps: obtaining geological and chemical prospecting data, and selecting a prospecting region based on the geological and chemical prospecting data; acquiring a high-resolution satellite image of the prospecting area, and acquiring geological, construction and mineralization alteration characteristics of an abnormal area of the prospecting area based on the high-resolution satellite image to construct a geological data set; a gold ore metallogenic area prediction model is constructed based on the long and short term memory network and the convolutional neural network; and inputting the geological data set into the gold ore metallogenic area prediction model for calculation, and generating a gold ore metallogenic area delineation result. Compared with a traditional geological prospecting method, the method has the advantage that the prospecting cost can be remarkably reduced by utilizing the high-resolution satellite image and the deep learning model to perform gold ore metallogenic prediction. Meanwhile, due to the fact that the accuracy of the prediction result is higher, unnecessary drilling and sampling work can be reduced, and resources and time are further saved.

The invention relates to the technical field of prospecting, in particular to an intelligent prospecting method based on multi-source information fusion and physical-data dual-drive, and the method comprises the steps: obtaining a target multi-source information database based on the granularity and reliability of multi-source information data in the target multi-source information database; performing complementary fusion on the multi-source information data according to the matched fusion level to obtain a multi-source information complementary fusion result; extracting mineral resource distribution information of the target area according to a multi-source information complementary fusion result, and generating an earth crust mineral resource refined distribution diagram of the target area by using the mineral resource distribution information; and based on the target multi-source information database and the earth crust mineral resource refined distribution map, generating a mineral resource information inference result of the target area by using a target identification system. According to the method, the target identification system can be utilized to perform deep mining on the earth crust mineral resource refined distribution map drawn based on the multi-source information, the mineral resource information inference result is obtained, the earth crust mineral resource exploration efficiency can be effectively improved, the resource exploration cost can be reduced, and strategic mineral resources can be accurately locked.

The invention belongs to the field of unconventional oil-gas exploration, and particularly relates to a lacustrine facies shale bed sequence stratigraphic division method based on principal component analysis. According to the method, rapid elemental analysis is carried out on a rock core sample by combining a portable energy dispersion X-ray fluorescence spectrophotometer, standardized processing is carried out on collected data, and key stratum change characteristics are extracted from the processed data by utilizing principal component analysis (PCA); by comprehensively considering multi-dimensional geological features such as lithology, mineral composition and oxidation-reduction conditions and combining existing geological background information and logging data, sequence units of lacustrine facies shale are divided. Compared with an existing method, the method has the advantages that a large amount of data can be efficiently processed in the complex sedimentary environment, man-made interference is reduced, data processing efficiency is improved, and more accurate and comprehensive technical support is provided for lacustrine facies shale bed sequence division.

The clamp comprises a support, a plate body, a first supporting leg plate and a second supporting leg plate, the support is connected with the plate body in a sliding mode, the first supporting leg plate is connected with the support in a rotating mode, and the second supporting leg plate is connected with the end, away from the first supporting leg plate, of the plate body in a rotating mode. According to the clamp, the exploration difficulty of technicians is reduced, the personnel safety is ensured, the exploration cost is reduced, the clamp can be quickly adapted to various types of plants, materials can be quickly purchased and quickly arrived at a construction site, and the smooth development of construction procedures is ensured.

This invention discloses a deep mineral exploration method based on multi-source information fusion and overlay, comprising: acquiring various trace element contents, fluid inclusion physicochemical parameters, and infrared spectral data from several sampling points in the exploration area; extracting geochemical indicators based on trace element contents, extracting spectral parameter indicators based on infrared spectral data and generating model prediction indicators, and selecting physical environment indicators from the fluid inclusion physicochemical parameters; interpolating non-sampling point grids in the exploration area using the indicator data from the sampling points for each indicator; obtaining a mineralization potential index for each grid point by weighting the indicators, and correcting the mineralization potential index based on the physical environment indicators; and determining the deep mineral exploration target area in the exploration area based on the geological background and the corrected mineralization potential. This invention establishes a dynamic mineral exploration model by comprehensively utilizing multi-source geoscientific information, and corrects and optimizes the model using multi-source monitoring data, thereby achieving accurate prediction of deep concealed ore bodies.

The application relates to the technical field of oil and gas exploration and development, and is a deep reservoir quality prediction method based on rock facies-geochemical coupling, which comprises the following steps: obtaining geochemical parameters and physical property parameters of a layer section to be predicted; taking the geochemical parameters and the physical property parameters of the layer section to be predicted as inputs, inputting a reservoir quality coupling prediction model, and outputting a porosity calculation value and a reservoir quality grade prediction result of the layer section to be predicted; and the reservoir quality coupling prediction model comprises a multivariate regression model related to the geochemical parameters, the physical property parameters and the porosity and a reservoir quality grade classification model. The application eliminates the multi-solution property of a traditional reservoir quality prediction method from a geological mechanism by means of strong coupling modeling of rock facies and geochemical parameters, and significantly improves the deep and super-deep reservoir quality prediction accuracy. Compared with the prior art, the application can significantly improve the drilling rate of high-quality reservoirs.

This invention discloses a core capture device based on ultra-short radius and multi-directional coring, belonging to the field of core capture technology. It includes an ultra-short radius directional drill bit, a multi-directional capture chamber, a micro-steering joint, a chamber switching valve assembly, and an ultra-short radius measurement and control terminal. The ultra-short radius directional drill bit is composed of a hemispherical cutterhead and an elastic deflection base. This invention solves the problem of excessive radial dimensions in traditional devices through structural miniaturization and power transmission optimization. It is adaptable to ultra-short radius wellbore operation scenarios such as radial branch wells and sidetracking infill wells. By overcoming the limitations of traditional unidirectional sampling, it can obtain core samples from reservoirs in different directions, accurately reflecting the anisotropy of physical properties such as permeability and porosity, supporting fine reservoir evaluation. For complex conditions such as shale and fractured formations, the sealing and jamming control design of the capture mechanism is optimized to reduce core loss, fracture, and drilling fluid contamination, ensuring core integrity.

This invention relates to the field of seismic exploration technology, and particularly to a method and apparatus for AVO trend correction of pre-stack gathers for interlayered shale oil reservoirs. The method includes: constructing seismic wavefield amplitude and travel time equations based on the steady-phase principle; establishing illumination weight coefficient equations based on deconvolution imaging conditions according to the seismic wavefield amplitude and travel time equations; determining illumination weight coefficients using the equations based on the forward-modeled wavefield propagation amplitude, receiver amplitude, shot amplitude, travel time from imaging point to shot point, travel time from imaging point to receiver point, travel time from shot point to receiver point, and angular frequency; and using the illumination weight coefficients to perform amplitude illumination compensation on the pre-stack gather seismic data to obtain the pre-stack gather imaging results after AVO trend correction. This invention can improve the accuracy of AVO in pre-stack seismic gathers, thereby identifying sweet spots in interlayered shale reservoirs with optimal reservoir and production potential, improving the reliability of geological models, reducing the risk of blind drilling, and saving exploration costs.

The invention discloses a complex structure area VSP wave field separation method, system and device and a medium, and the method comprises the steps: taking actual VSP observation data as the basis, and precisely obtaining stratum velocity information; multi-source geological exploration information is utilized to construct a plurality of groups of possible forward models to comprehensively cover possible features of a complex structure, and a target model obtained through subsequent matching and screening can accurately reflect a real geological structure, so that the problems of single model data source and inaccurate representation in an existing method are solved, and the method is suitable for large-scale popularization and application. A reliable model basis is provided for wave field separation; according to the method, an optimally matched target model simulation wave field is taken as a reference basis, propagation paths and wave field characteristics of target waves such as reflected longitudinal waves and interference waves are determined, the target waves and the interference waves which are overlapped and confused in a complex structure area can be accurately distinguished, the influence of the interference waves is effectively eliminated, and the accuracy and the reliability of the complex structure area are improved. The technical problems of strong VSP wave field interference and low separation precision in a complex structure area are effectively solved.

The application discloses a coal seam lithium-rich layer detection method based on well logging curves and statistical analysis, and belongs to the technical field of mineral resource exploration. The method comprises the following steps: selecting a series of representative coal seam positions for systematic sampling, and preparing the samples into piston samples and powder samples; determining the kaolinite and lithium content of the powder samples, and distinguishing lithium-rich and ordinary coal samples; systematically determining various geophysical response parameters of the piston samples, such as density, resistivity and wave velocity, and screening out sensitive discrimination indexes for lithium enrichment; setting a window size, calculating the moving statistical value curves of the density, resistivity, wave velocity and other well logging curves sensitive to the lithium-rich layer, calculating principal components, and using the selected principal components and a support micro machine to train a prediction model, so as to predict the lithium-rich layer in the coal seam. The application combines geophysical logging, statistical analysis, principal component analysis and a support vector machine, and provides a fast, economical and non-destructive means for lithium resource exploration in coal.

The invention discloses a rare earth ore deep exploration method and system based on time-frequency electromagnetism, and a storage medium. The exploration method comprises the following steps: identifying a target region in a to-be-explored region; acquiring exploration data of a target area by using a time-frequency electromagnetic method to obtain first time domain data and first frequency domain data; calculating the apparent resistivity of the target region according to the first time domain data, and calculating a total longitudinal conductivity parameter based on the apparent resistivity; an Occam inversion model related to the total longitudinal conductivity parameters is constructed; performing dual-frequency phase calculation and dual-frequency amplitude calculation according to the first frequency domain data to obtain a frequency parameter set of the target region; fitting the Occam inversion model, and performing polarizability inversion based on the frequency parameter set; and delineating the deep geophysical prospecting anomalous body in the target region based on the polarizability inversion result. According to the method, deep rare earth ore can be identified by using the time-frequency electromagnetic technology, the number of exploration drilling wells is reduced, the exploration cost is reduced, the prospecting time is shortened, and the exploration efficiency of the hidden rare earth ore deposit in the thick coverage area is improved.

The invention discloses a method for constructing metamorphic and ancient iron-copper polymetallic ore deposit mineralization alteration zoning and determining the prospecting direction, and belongs to the technical field of mineral resource exploration. The method comprises the following steps: (1) carrying out large-scale (1: 200 or larger scale) mineralization alteration mapping on a typical middle section and a typical section of a target ore deposit; (2) on the basis of detailed chronology research results, systematically carrying out typical rock and ore comprehensive identification, and finding out the composition and characteristics of a metallogenic system; (3) summarizing mineralization alteration zoning rules of different mineralization systems based on results of the previous steps; (4) comprehensively constructing a mineralization alteration zoning mode; and (5) based on the constructed mineralization alteration zoning mode, proposing a deep prospecting direction of the target ore deposit. And an important basis is provided for prospecting exploration of the deep edge part of a mine field or an ore deposit.

The present invention discloses a method for quickly delineating porphyry deposit ore bodies using chlorite trace element content, and relates to the technical field of ore deposit exploration. The method includes performing multiple three-dimensional spatial sampling from a known porphyry deposit mining area to obtain multiple samples; grinding multiple samples into thin slices, screening thin slices containing chlorite; performing scanning electron microscope analysis to screen out samples to be tested; performing electron probe analysis to obtain the content of major elements; performing LA‑ICP‑MS analysis to obtain the content of trace elements; screening target data; and constructing a porphyry deposit chlorite exploration marker equation based on the relationship between the trace elements in the target data and the spatial information of the corresponding samples. The present application greatly improves the amplitude and range of element anomalies, improves the efficiency and accuracy of porphyry deposit prospecting, effectively proposes forward-looking predictions for prospecting in the deep edge of the deposit, reduces exploration costs and time, and efficiently and reasonably utilizes mineral resources.

The embodiment of the present application provides a method and device for determining the oil and gas transport capacity of a strike-slip fault during the accumulation period in sections, the method comprising: dividing the strike-slip fault into multiple sections according to characteristic information of the strike-slip fault in a seismic profile; determining the oil and gas transport capacity of each strike-slip fault during the accumulation period according to the angle between the principal stress direction and the fault strike of each strike-slip fault during the accumulation period, the average density of sedimentary rocks, the density of formation water, the fault dip, the burial depth of the fault point during the accumulation period, and the mud content of the fault rock; determining the strike-slip fault that can transport oil and gas during the accumulation period according to the oil and gas transport capacity of each strike-slip fault during the accumulation period and a preset oil and gas transport capacity screening rule; the present application can accurately calculate the oil and gas transport capacity of different fault positions during the accumulation period in areas where strike-slip faults are developed, thereby improving the success rate of drilling and saving exploration costs.

The invention belongs to the technical field of metallogenic prediction, and discloses a metallogenic prediction method based on fine sample segmentation and a small sample learning strategy. According to the method, a robust small sample learning framework is provided, the capacity of labeled data is expanded through data enhancement, and the model is pre-trained and finely adjusted by adopting transfer learning, so that the model has excellent prediction efficiency, the problem of overfitting of the model is inhibited to a certain extent, and the uncertainty of model prediction is reduced. The small sample learning framework balances the data set through SMOTE, and the classification precision of the data set in the training process is improved. The prediction points of the small sample learning framework are arched, which is beneficial for focusing potential targets with high probability and low uncertainty. The small sample learning framework has the ability to control overfitting and produces a high confidence exploration target with a low level of uncertainty.

The present application relates to a kind of green prediction method and device of mineral based on cross-domain self-adaptation and multimodal fusion, belong to mineral resources exploration and remote sensing technical field.Multiple-source data is obtained, input transfer learning module learns transferable model parameters, the data obtained is fused through modal gating network, then through graph construction model constructs line mapping, and is enhanced using graph convolution network;After enhancement, multimodal data is input into the Transformer encoder of embedded graph convolution network, extract the spatial topology structure and spectral feature of mining area, obtain the potential probability graph of ore body through the prediction head prediction;Environmental risk assessment model is built, " maximum of ore body prediction accuracy " and " minimum of environmental risk " are realized through multi-objective optimization, and the accuracy of ore body prediction is verified through drilling activity.This method not only improves the regional generalization ability of model, but also realizes green mineral exploration and the interpretability of result, improves the precision, efficiency and sustainability of mineral exploration.

The invention discloses a muon absorption imaging geological interpretation method and device, equipment, a medium and a product, and relates to the field of mineral resource exploration, and the method comprises the steps: obtaining muon absorption imaging data, geological data and geophysical prospecting data of a research area; according to the muon absorption imaging data, performing feature extraction by adopting a visual feature extraction method and a machine learning algorithm to obtain gradient features, continuity features, morphological features and clustering features; according to the gradient features, the continuity features, the morphological features and the clustering features, interpreting the metallogenic favorable fracture, the lithologic interface and the ore control rock mass to determine the spatial morphology and attribute parameters of the geology in the research area; according to the geological data and the geophysical prospecting data, reliability verification is carried out on interpretation results of the metallogenic favorable fracture, the lithologic interface and the ore control rock mass, and the feature extraction process is adjusted according to the reliability verification result and then interpreted again. The method is high in automation degree and adaptability, and improves the precision and reliability of geological interpretation.

The invention relates to the technical field of mine data processing, and discloses a sample data set generator for mine resistivity deep learning inversion, comprising a model construction module used for constructing a plurality of medium resistivity models and customizing resistivity values corresponding to the medium resistivity models; constructing a corresponding mine working face structure model; the data simulation module is used for setting basic exploration parameters according to each mine working face structure model; the calculation module is used for calculating a theoretical resistivity observation value according to the exploration basic parameters and the mine working face structure model; the processing module is used for identifying a corresponding noise level and determining a noise value, and adding the noise value to the theoretical resistivity observed value to form a corresponding simulated resistivity observed value; and the data generation module is used for identifying a corresponding medium resistivity profile map according to each simulated resistivity observation value, and packaging each simulated resistivity observation value and the corresponding medium resistivity profile map into a specified format.

A method and apparatus for evaluating reservoir oil-bearing potential based on gas logging data, the method comprising: acquiring raw total hydrocarbon data and gas logging influencing factor data of a target formation in a study area; determining correction coefficients for each gas logging influencing factor, and obtaining corrected total hydrocarbon data based on the correction coefficients and raw total hydrocarbon data; calculating a similarity factor and a saturation factor using the corrected total hydrocarbon data; and determining the oil-bearing potential of the target formation based on the similarity factor and the saturation factor, and using a pre-established gas logging data interpretation chart in the study area.

This invention discloses a hybrid attention network method for 3D prediction of deep-sea rare earth elements, integrating spatial and depth data, and relating to the interdisciplinary fields of artificial intelligence and marine mineral resources. The method first acquires multi-source environmental and depth data, which are then integrated, cleaned, spatially aligned, interpolated, and standardized to construct a model input containing environmental feature image patches and standardized depth values. Spatial feature vectors are extracted using a convolutional neural network, and depth feature vectors are encoded using a multilayer perceptron. The two types of features are concatenated, and dynamic weights are generated and weighted using an attention network to obtain a weighted feature vector. This weighted feature vector is input into the prediction head module, and the model is trained using a loss function, optimizer, and early stopping mechanism. The trained model is applied to a 3D mesh of the target region, and after inference prediction and de-standardization, the predicted 3D rare earth element content is output. Simultaneously, the feature contribution is analyzed through an interpretable model.