21 results about "Stratigraphy" patented technology

The invention relates to the technical field of geological survey, in particular to a rapid stratum disease risk detection method based on a ground penetrating radar, and solves the technical problem of depth positioning misalignment caused by propagation parameter fluctuation due to heterogeneity of an underground medium in stratum disease detection of the ground penetrating radar in the prior art. The method comprises the following steps: acquiring a reflected wave signal at each acquisition moment through a ground penetrating radar; performing signal decomposition processing on the reflected wave signal, separating an intrinsic mode component representing dielectric noise, and performing feature extraction on the intrinsic mode component to determine a dielectric noise intensity index; constructing a dielectric noise intensity sequence according to the dielectric noise intensity index, and performing spatial-temporal characteristic analysis to determine the confidence coefficient of stratum diseases; and according to the dielectric noise intensity index and the stratum disease confidence coefficient, in combination with a pre-calibrated stratum average dielectric constant, constructing a depth correction weight, carrying out adaptive correction on the original stratum disease depth, and outputting the corrected stratum disease depth.

The application discloses a kind of based on sequence stratigraphy and seismic sedimentology's braided river channel reservoir fine characterization method, comprising the following steps: obtaining target area core, well logging and high-resolution three-dimensional seismic data;By analyzing the sedimentology mark and logging facies mark in the target area, to determine the sedimentary facies type and sedimentary microfacies division;Using lithology and well logging curve, using the theory of sedimentology, high-resolution sequence stratigraphy identifies sequence interface type and feature, and carries out high-resolution sequence stratigraphic sequence division;Division reservoir architecture interface;According to the internal structure of sedimentary system, sedimentary facies distribution, focus on high-frequency sequence and sedimentary body scale, generate the braided river channel reservoir fine characterization result of target area architecture unit.The present application effectively solves the deficiencies of prior art in reservoir fine description, boundary identification and parameter inversion, improves the efficiency and success rate of oil and gas reservoir development.

This invention relates to a rapid method for three-dimensional structural restoration by eliminating structural anomalies, belonging to the field of geological structural restoration technology in oil and gas field exploration. The method first establishes a three-dimensional stratigraphic fault model based on the interpreted horizon and interpreted faults of the target segment. Then, according to the seismic network, the model is decomposed into L and XL directions, and structural restoration is performed in both directions respectively. Finally, the restoration results of the two directions are combined to obtain the structural restoration result of the target interface. This method not only preserves the complete information of the stratigraphy but also eliminates structural anomalies, improving the accuracy and reliability of structural restoration.

A method for curating a machine learning training library of calcareous nannofossil images for automated biostratigraphic analysis. The method includes receiving a plurality of images of various source materials from a target geologic time period. A subset of images is selected, comprising images from both a hydrocarbon-rich region and a hydrocarbon-poor region. A training library is generated based on this subset, enabling the development of machine learning models capable of accurate fossil identification and classification across diverse geological settings.

The invention discloses a method for determining a geologic horizon of a PSV wave seismic event, and relates to the technical field of petroleum and natural gas exploration and development, and the method comprises the following steps: S1, carrying out the rotation orientation processing of a horizontal component and a vertical component; s2, extracting an upgoing wave field of the PSV wave; s3, non-zero offset PSV wave imaging and non-zero offset PSV wave corridor superposition are carried out; s4, determining the geologic horizon of the PSV wave corridor stacked section; and S5, determining the geologic horizon of the three-dimensional seismic PSV wave event. According to the method, a high-precision extraction method of the non-zero offset PSV upgoing wave field is innovated, and effective separation of the complex wave field is ensured; meanwhile, a new corridor stacked section manufacturing method and a new geologic horizon determining method are created, the situation that logging curve missing and environmental quality affect the reliability of geologic horizon determination is avoided, the problem that calibration difficulty is large due to the fact that zero-offset PSV wave signals are weak is solved, more reasonable PSV wave geologic horizon determination can be obtained, and the accuracy of geologic horizon determination is improved. And a basic guarantee is provided for multi-wave multi-component oil gas earthquake prediction application.

This application relates to the field of geological engineering technology. To address the problems of time-consuming parameter learning and errors caused by reliance on subjective experience in undirected probabilistic graphical geological model construction, a method for constructing undirected probabilistic graphical geological models based on spatial location encoding is disclosed. This method includes: determining the geological modeling range based on known borehole data; discretizing the geological modeling range into a grid structure; filling the corresponding grid with stratigraphic data from the known borehole data according to soil type; spatially encoding the unknown grids based on the spatial relationship between the grid structure and the known borehole data; assigning an initial state to the unknown grids based on the spatial relationship between the grid structure and the known borehole data to obtain the initial stratigraphy and constructing an undirected probabilistic graphical geological model; and constructing a data-driven parameter optimization method based on the spatial location encoding results to learn the parameters of the undirected probabilistic graphical geological model and obtain the optimal parameters. This method improves the accuracy of undirected probabilistic graphical geological model construction.

This invention discloses a method, system, and application for fine characterization of multi-scale sedimentary cycles facies space. This method combines variational mode decomposition (VMD) with synchronous compression optimal basis wavelet transform (MCB) to propose a workflow for multi-scale sedimentary cycle characterization. First, the relationship between geological structures at different scales and intrinsic mode functions (EMFs) is theoretically derived, and a method for determining the number of EMFs is proposed. Considering computational efficiency and the lateral continuity of the decomposition results, the variational mode decomposition algorithm is improved by determining the number of EMFs and their center frequencies based on the smoothed reflection coefficient amplitude spectrum. Based on this, multi-scale sedimentary cycle characterization is achieved by combining MCB with ridge extraction. The key parameters in the processing of this invention are all extracted from seismic data, making the operation simple, computationally fast, and suitable for processing massive amounts of seismic data. This provides an important tool for subsequent sequence stratigraphy research.

Provided is a virtual drilling modeling method and system based on outcrop fine characterization. The method includes: selecting multiple typical sedimentary stratigraphic point positions respectively, and using an outcrop fine observation technology to characterize distribution characteristics of lithology, occurrence, thickness, and an extension range of each outcrop layer at the typical sedimentary stratigraphic point positions; establishing each vertical sequence stratigraphic histogram according to the distribution characteristics, the vertical sequence stratigraphic histogram being used to reflect vertical changes of actual stratigraphy; constructing a modeling data set according to each of the vertical sequence stratigraphic histograms and preset sample drilling core columns; and based on machine learning and interpolation operation methods, constructing a three-dimensional geological model according to the modeling data set. Through the high-precision outcrop characterization technology, the present disclosure can establish an accurate three-dimensional geological model with lower cost and higher efficiency without actual drilling.

This invention relates to the field of oilfield development technology and discloses a well-seismic combined method for reorganizing reservoir stratigraphy in sandstone oilfields, comprising the following steps: S1, acquiring well logging data, seismic data, and geological data to establish a high-resolution sequence stratigraphic framework; S2, using well-seismic combined technology to draw sedimentary facies maps and identify the distribution characteristics and development differences of reservoir sand bodies; S3, analyzing the dynamic development effect of the reservoir, including water cut, recovery rate, and remaining oil distribution development indicators; S4, optimizing the stratigraphic combination method, sealing high water-cut layers, and reorganizing effective development stratigraphic layers; S5, adjusting the well network deployment, combining new drilling and old well conversion for injection, and optimizing well spacing and injection-production relationship. By using well-seismic combined technology to finely segment reservoirs, optimize stratigraphic layers and injection-production well network deployment, and combine dynamic monitoring to adjust injection-production configuration in real time, the problem of inter-layer interference is solved, water injection displacement efficiency is improved, and high efficiency and stability of oilfield development are achieved.

The invention relates to the fields of stratigraphy, statistics, geophysics and the like, in particular to a method for calculating the total gas content of a coal seam through a logging curve. A method for calculating the total gas content of a coal seam through a logging curve is characterized in that a gas content prediction model composed of six parameters including natural gamma GR, a photoelectric index PE, compensation sound waves AC, compensation neutrons CNL, lithologic density DEN and deep lateral resistivity RD is constructed and used for predicting the total gas content of the coal seam. The method realizes continuous and accurate calculation of the total gas content of the coal seam in the Ordos basin through coupling modeling of the response sensitive parameters of the total gas content of the coal seam, and is an economical and efficient method for obtaining the total gas content of the natural gas in the coal seam.

This invention discloses a method for evaluating the quality of deep oil shale based on segmented well logging inversion, belonging to the technical field of geophysical well logging interpretation and unconventional oil and gas resource evaluation. The steps are as follows: (1) data input; (2) data preprocessing; (3) joint segmentation of stratigraphy and depth; (4) segmented modeling; (5) inversion calculation; (6) dual-parameter threshold discrimination; (7) quality classification; (8) result output. By including multiple sedimentary segments in the evaluation object of deep oil shale, oil content and TOC inversion models are established for different target stratigraphic positions and different model selection depth ranges. The model can be used to supplement the oil content inversion of the 1000–2500m depth segment and improve the inversion accuracy of deep oil shale in the 1000–2500m depth segment. At the same time, it retains the advantages of continuous, economical and high-resolution well logging data, and can realize continuous evaluation of deep oil shale in the 1000–2500m depth segment, providing a more reliable technical means for accurate identification, quality classification and resource evaluation of deep oil shale.

This invention discloses a method for prototyping and reconstructing modified depressions, comprising the following steps: dissecting and analyzing the depression structure in the study area to identify its structural characteristics; identifying the substratigraphic framework of the main depositional periods in the study area; identifying and statistically analyzing the fault points and displacements within the depression structure, and reconstructing the vertical displacements of the faults; identifying the vertical uplift of the depression structure and reconstructing the amount of vertical uplift; identifying the horizontal extension of the depression structure and reconstructing the amount of horizontal extension; tracing and characterizing the residual stratigraphic boundaries within the study area, and extracting key occurrence information of the residual strata; and based on the extracted key occurrence information of the residual strata, reconstructing and reconstructing the original distribution morphology of the target strata within the depression structure. This invention can fully utilize the sedimentary and structural information of the basin's residual stratigraphy, and through detailed characterization and constraint of sedimentary boundaries, accurately and conveniently reconstruct and reconstruct modified depressions.

The application discloses a prestack seismic reflection analysis method based on an improved focal point loss, which is applied to the field of seismic stratigraphy, and aims at the fact that the existing depth clustering method often ignores the representation ability of potential features and always has the sample misclassification problem in the clustering layer. First, prestack seismic data is obtained according to actual work area data and time window data, so as to prepare a network training sample data set. Then, the data set is iteratively trained by using a DCIF network model, the target distribution of the clustering layer is regarded as the real label distribution, the improved focal point loss is introduced into the clustering module, and is used in an unsupervised manner. The method is combined with the two modules to optimize the two modules, and learns the representation for the depth clustering in an end-to-end manner, and directly generates a seismic facies map.

A method for curating a machine learning training library of calcareous nannofossil images for automated biostratigraphic analysis. The method includes receiving a plurality of images of various source materials from a target geologic time period. A subset of images is selected, comprising images from both a hydrocarbon-rich region and a hydrocarbon-poor region. A training library is generated based on this subset, enabling the development of machine learning models capable of accurate fossil identification and classification across diverse geological settings.

The invention discloses a fine-grained shale deposition structure identification method and device, electronic equipment and a medium. The method comprises the following steps: determining a research target layer, obtaining a continuous rock core column, and analyzing sedimentary structure characteristics of the rock core column in combination with logging curve characteristics; combining a correlation coefficient method to verify that shale deposition in a research area is influenced by an astronomical orbit period; the method comprises the following steps: establishing a scale for researching the absolute geological age of a stratum through petrostratigraphy, magnetic stratum and isotope analysis; carrying out high-resolution astronomical tuning on the slope and the age difference scale to obtain the duration of a deposition sequence, and recovering the deposition rate by establishing an age model; and establishing an evolution energy spectrum of the shale deposition structure and the age difference level deposition rate, and dividing a histogram of the deposition structure of the research target layer. According to the method, the development characteristics of the sedimentary structure of the fine-grained mixed sedimentary rock in the area can be conveniently, accurately and qualitatively researched by qualitatively identifying the sedimentary structure of the multi-element mixed sedimentary rock and quantitatively screening the distribution range of the sedimentary structure of the multi-element mixed sedimentary rock.

This invention relates to the field of geological surveying technology, specifically to a rapid detection method for stratigraphic disease risk based on ground-penetrating radar (GPR). This method solves the technical problem in existing GPR techniques where the inaccurate depth positioning is caused by propagation parameter fluctuations due to the non-uniformity of the underground medium during stratigraphic disease detection. The method includes: acquiring reflected wave signals at each acquisition time using GPR; performing signal decomposition processing on the reflected wave signals to separate the intrinsic mode components characterizing dielectric noise, and extracting features from the intrinsic mode components to determine the dielectric noise intensity index; constructing a dielectric noise intensity sequence based on the dielectric noise intensity index and performing spatiotemporal feature analysis to determine the stratigraphic disease confidence level; constructing a depth correction weight based on the dielectric noise intensity index and the stratigraphic disease confidence level, combined with a pre-calibrated average dielectric constant of the stratigraphy, adaptively correcting the original stratigraphic disease depth, and outputting the corrected stratigraphic disease depth.

This invention discloses a method for creating a micro-paleomorphic map of stratigraphy using a combination of well and seismic data, comprising the following steps: S11 Obtaining stratigraphic marker layers for correlation using drilling data within the area; S12 Comprehensive drilling calibration, conducting isochronous surface tracing and interpretation of seismic sequences, and establishing an isochronous stratigraphic framework using a combination of well and seismic data; S13 Reconstructing the pre-depositional paleomorphic features of the target layer using layer flattening technology; S14 Selecting a layer segment, calculating the stratigraphic thickness using the top and bottom data of the target layer, and obtaining the true seismic stratigraphic thickness after drilling correction; S15 Compacting and correcting the true seismic stratigraphic thickness to obtain the original sedimentary thickness of the stratigraphy; S16 Applying the original sedimentary thickness of the stratigraphy to generate a micro-paleomorphic map using Surfer mapping software. This invention is applicable to the field of petroleum exploration technology, is highly operable, and makes full use of well and seismic data for three-dimensional exploration areas with fewer wells. The micro-paleomorphic features reconstructed by this method can finely depict the paleotopographic features of the beach bar sandstone deposition period and analyze the planar distribution pattern of the beach bar sandstone.

This invention provides a method and system for delineating lead-zinc ore target areas based on the analysis of alteration mineral mapping results. It relates to the field of computer technology. First, it acquires regional alteration mineral mapping results and information on regional ore-controlling conditions. Then, using the lateral distribution span, vertical burial depth, and mineral assemblage in the regional alteration mineral mapping results, combined with regional geological evolution history data, it constructs an alteration mineral zonation sequence. Next, based on the spatial distribution morphology of mineralization structures, the physical and mechanical properties of stratigraphy, and the heat transfer path of magmatic activity in the regional ore-controlling conditions information, it quantifies the synergistic response intensity of various ore-controlling conditions to the formation of alteration minerals. Combining the above results, it locks the spatial location and extension range of lead-zinc ore mineralization windows. Finally, based on the ore-forming windows and element enrichment states, it delineates the spatial range of the target area and optimizes the boundaries, generating lead-zinc ore target area delineation results. This method can effectively guide lead-zinc ore exploration and development, improving resource discovery rate and development efficiency.

The present invention discloses a seismic full horizon tracking method based on a multi-attribute knowledge graph, which is applied to the field of oil and gas exploration. Aiming at the problem that the existing seismic full horizon tracking methods are difficult to effectively represent the stratigraphic relationships in seismic data, resulting in the phenomenon of time slippage easily occurring in areas with complex geological structures such as faults in the tracking results and being difficult to meet the actual application requirements. The present invention first uses the dynamic time warping algorithm to construct horizon segments, and calculates the fault attribute and dip attribute by using transfer learning and gradient structure tensor respectively. Subsequently, the multi-attribute knowledge graph is used to characterize the stratigraphic relationships in seismic data. Finally, under the constraints of the fault attribute, dip attribute and stratigraphic relationship, the nodes in the initial multi-attribute knowledge graph are continuously fused, and a complete horizon plane is obtained on the basis of conforming to seismic stratigraphy. Experiments show that the method of the present invention can effectively represent the stratigraphic relationships of seismic horizons in the underground three-dimensional space and accurately track the horizons located at complex geological structures such as faults.

A method of computer-based fossil identification with deep learning processes is disclosed, which is thought to bring great convenience and innovation to paleontological and stratigraphic studies. According to the method of the present invention, from the photographs of microfossils (single-celled fossils) belonging to the acritarch and chitinozoan groups, it is possible to automatically predict which genus the individual in the photograph belongs to. In micropaleontological studies, the genus and species determination of fossils can be made by experts under the microscope. Fossil identification is a time-consuming and highly specialized process. With this system developed using deep learning and computer vision techniques, these problems are prevented, the margin of error observed in the definitions can be reduced, and it may be sufficient to have a lower level of expertise required during the definitions.

This invention relates to the field of artificial intelligence technology and discloses a method and system for real-time 3D paleontological reconstruction based on artificial intelligence and multimodal interaction. It aims to solve the problems of fragmented multi-source data, low automation, limited interaction methods, and lack of intelligent reasoning capabilities in existing technologies, which lead to strong subjectivity and difficulty in dynamic verification. The method includes: constructing a four-dimensional unified representation framework integrating fossil point clouds, stratigraphy, isotopes, and extant analog data, and generating a structured knowledge graph; using conditional generative adversarial networks to complete missing structures under biomechanical and evolutionary rules constraints, generating an anatomically consistent 3D morphological model; deploying natural language and virtual reality interfaces to achieve multimodal human-computer collaborative interaction; and triggering incremental learning based on user feedback and new evidence to complete real-time iterative optimization. This system integrates the above modules and scientific verification mechanisms, enabling high-fidelity, interactive, verifiable, and continuously evolving 3D dynamic paleontological reconstruction.