9results about How to "Lower Exploration Costs" patented technology

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 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.

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.