27 results about "Sedimentology" patented technology

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.

The invention discloses a device and a method for acquiring a water tank experiment sedimentary profile based on a frozen profile technology, and belongs to the technical field of sedimentology and experimental simulation, and the device and the method are characterized in that stable freezing extraction of the sedimentary profile in a water tank is realized through a wedge-shaped freezing device. The device is internally provided with a liquid nitrogen channel and is provided with an external control interface, and a section freezing state can be kept by injecting liquid nitrogen in batches, so that a complete sedimentary section sample is obtained. Meanwhile, the invention provides a corresponding profile acquisition method, and emphasizes speed control, friction force control, liquid nitrogen injection rhythm and pull-out stability of the insertion device, so as to ensure that an undisturbed frozen profile sample is acquired. According to the method, disturbance to a sedimentary body structure can be remarkably reduced, and the precision and analysis efficiency of profile data are effectively improved, so that reliable technical support is provided for source-sink system and sedimentary geomorphology research.

The invention discloses a multi-source unmixing analysis method and system for a geological source-sink system based on a dzmix inverse Monte Carlo model, relates to the technical field of tectonic sedimentology and data modeling crossing, and collects and arranges zircon U-Pb chronological data of a target horizon mixed sample in a research area and zircon U-Pb chronological data of a basin peripheral material source area. Establishing a multi-source area big data chronology database of the research area; and establishing standard stratum characteristics in combination with regional sedimentary characteristics and petrology characteristics. According to the method, the contribution proportion of the multi-source substances in the geological source-sink system is quantitatively recovered, the defects that a traditional source-sink analysis method depends on priori end member selection and is easily influenced by personal errors are overcome, the inverse Monte Carlo model is used for carrying out iterative simulation for multiple times, the simulation effect is gradually optimized, the accuracy and reliability of a de-mixing result are ensured, and the method is suitable for large-scale popularization and application. And a more scientific and accurate technical means is provided for geological research and resource exploration.

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.

The application discloses a method for determining the migration degree of a deep-sea channel, a storage medium and computer equipment, and comprises the following steps: dividing a deep-sea composite channel on a deep-sea channel planar distribution map into a plurality of curved segments; for each curved segment, the following steps are performed: determining the vertical migration amount and the lateral migration amount of a deep-sea single channel corresponding to the curved segment on a seismic amplitude profile corresponding to the curved segment; calculating a channel migration index of the curved segment according to the vertical migration amount and the lateral migration amount of the deep-sea single channel corresponding to the curved segment; and analyzing the migration degree of the deep-sea channel of the curved segment according to the channel migration index of each curved segment. The migration evolution degree of the deep-sea single channel can be quantified through the channel migration index, which not only enriches the morphological parameters of the deep-sea channel, but also plays an important supporting role in the quantitative sedimentology and sedimentary system research of the deep-sea channel.

The invention relates to the technical field of geology and sedimentary data analysis, in particular to a sedimentary rock particle analysis method and system based on an opencv visual library. The method comprises the following steps: firstly, acquiring a high-resolution digital image of a sedimentary rock sample, and preprocessing to enhance the characteristics of clastic particles; processing the image by using an opencv library, automatically identifying and segmenting a single debris particle, and calculating the equivalent diameter of the single debris particle; finally, an analysis report containing particle distribution and particle size statistical information is automatically generated. According to the method, image acquisition, processing, analysis and report generation are integrated into a continuous automatic process, a traditional manual microscope observation and manual measurement mode is replaced, the analysis efficiency is remarkably improved, and the method is suitable for large-scale sample processing. Recognition and measurement are executed through a standardized algorithm, subjective errors are effectively reduced, objectivity and consistency of analysis results are guaranteed, and an efficient and reliable technical means is provided for sedimentary rock granularity analysis.

The application discloses a kind of based on genesis method's thin layer stratum thickness prediction method, system and medium, it is related to stratum thickness prediction technical field, the method includes: S10, stratum distribution main control factor analysis;S20, structural boundary delineation;S30, in combination with stratum distribution law and seismic delineation structural boundary, qualitative recovery is carried out to stratum geological model, obtains stratum thickness trend model;S40, in combination with stratum thickness data of drilled well and stratum thickness trend model, establishes stratum thickness prediction data model, and iteration is carried out to it;S50, according to well deviation of actual drilling, calculates the well bottom coordinate of the top of target layer;S60, calculates the key parameter of well bottom and coordinate boundary, then it is input into stratum thickness prediction data model, and stratum thickness is predicted.The present application introduces the principle of sedimentology, genesis method predicts stratum thickness, and is strong in logic, and the method is more reasonable;Meanwhile, the present application reduces the quality requirement of seismic data, and is strong in compatibility, avoids excessive processing of seismic data.

The invention relates to the technical field of geophysical logging and sedimentology intelligent interpretation, and particularly discloses a method for intelligently segmenting and identifying sedimentary microfacies based on a logging curve. The method comprises the steps that a gamma ray curve and cleaning rule configuration are acquired, and a coarse segmentation segment set is generated through data cleaning, standardization and filtering processing; a section sample set is generated through interface alignment, boundary fine adjustment and section judgment in combination with a logging interpretation conclusion; feature extraction is carried out based on operation period dependence, and a segment classification result is generated through rule classification and model inference fusion processing; and finally, outputting a sedimentary microfacies type result and forming closed-loop feedback through sequence splicing, morphological rule mapping and configuration parameter self-adaptive updating. According to the method, automation and adaptive optimization of the sedimentary microfacies identification process are realized, and the identification accuracy and the engineering application efficiency are effectively improved.

The invention belongs to the field of carbonatite sedimentology, and particularly relates to a protolith recovery method for transforming cloud rock from karst. The method comprises the following steps: determining petrology characteristics of a target sample by utilizing microscopic imaging and cathodoluminescence, including cloud rock types and development characteristics, and determining a protolith type deposition sequence; on the basis, geochemical research is carried out by combining various microtechnologies (carbon and oxygen) isotope analysis, microelement analysis and rare earth element analysis), the effectiveness of geochemical data is improved, and geological constraints are provided for interpretation of the geochemical data, so that the karst erosion process of the protolith is more accurately determined, and protolith recovery is facilitated.

Extraction of gravel morphological parameters is an important research content of sedimentology, and due to the fact that the work requires to collect a large amount of sample data, a traditional working method is tedious in process and low in efficiency; and the geological action is a gradual change process, and the gravel form is ever-changing and is always one of the properties which are most difficult to describe and quantify of an object, so that the method has important practical significance on intelligent acquisition of the gravel. According to the method, a gravel image sample database is established, an intelligent gravel identification model is constructed based on an artificial intelligence (AI) method, and intelligent identification and extraction of gravel morphological parameters are realized, so that powerful methods, means and data support are provided for geologists to carry out related researches; the method has a wide application prospect in the aspects of studying the material source, the sedimentary environment, the paleo-geographic characteristics, the structural environment, the paleo-environment transition and the like of the sedimentary basin.

The invention discloses a method for recognizing drilling fault hanging wall and footwall stratigraphic units through rock slice imaging. The method comprises the steps that firstly, the stratigraphic distribution condition of a target area under a normal sequence is obtained, and then orderly sampling is conducted on a fault hanging wall, fault breccia and a fault footwall at the fault position; the method comprises the following steps: grinding a sample into a slice, imaging the slice under a microscope, inputting the slice into a computer, identifying an image to obtain a lithologic combination of a fault breccia, a fault hanging wall and a fault footwall, and establishing a corresponding sedimentary facies according to the lithologic combination of the fault hanging wall and the fault footwall and sedimentary characteristics; comparing the obtained lithology combination and sedimentary facies with the stratigraphic unit information of the normal sequence, and preliminarily judging the stratigraphic units corresponding to the hanging wall and the footwall; and correcting the obtained stratum unit by using the lithology combination of the breccia in the fault breccia. The method comprises the following steps: observing fault two disks and fault breccia cores, grinding slices, imaging in a computer, and identifying and comprehensively analyzing and identifying upper and lower disk stratigraphic units of the fault.

The application discloses a tight sandstone dessert reservoir prediction method based on seismic sedimentology, and relates to the technical field of reservoir prediction in oil exploration. The tight sandstone dessert reservoir prediction method based on seismic sedimentology comprises the following steps: pre-processing well logging data; drawing a seismic-geological interpretation profile graph; obtaining a structure graph; obtaining a fine layer velocity model; performing well logging curve standardization processing; performing forward simulation; performing reservoir inversion; extracting attributes; and comprehensively performing reservoir prediction according to the attributes and the inversion. The method improves the accuracy and precision of reservoir prediction, and improves the efficiency and economic benefits of data processing and exploration and development.

The invention relates to the field of sedimentology research, in particular to a fine material source analysis method for a mountain-building basin, which is realized by adopting a Monte-Carlo simulation method, and the Monte-Carlo simulation method realizes the analysis process of a plurality of sub-source areas on sedimentary basin material sources by utilizing highly decomposed age spectrum new subclasses. Height decomposition of the method is completed by clustering and heat map analysis of age spectrum data of debris zircon in a sedimentary basin and a source region, and height decomposition of complex age spectrum data in a source-sink process is realized by decomposing complex age spectrum clusters of the sedimentary basin and the source region into different age spectrum new subclasses. The problem that hybrid data of the sedimentary basin is analyzed by using hybrid data of a source region traditionally is avoided, age spectrums of the sedimentary basin and the source region are clear at a glance, and potential uniformity or diversity characteristics of the sedimentary basin and the source region are easy to identify.

The invention discloses a seismic lithogenous phase prediction method based on semi-supervised learning, and belongs to the technical field of seismic sedimentology. Analyzing main diagenesis types of compact sandstone at a target layer by comprehensively using analysis test data, rock physical experiment data, logging data and seismic data, establishing a diagenesis evolution sequence and a diagenesis phase division scheme, and determining rock core diagenesis phases; a logging curve sensitive to the lithogenous phase is screened out, and vertical distribution of the lithogenous phase on a single well is evaluated through logging; determining diagenetic phase sensitive seismic elastic parameters by means of rock physical analysis, correlation analysis and analysis of relation among diagenetic characteristics, mineral components and seismic elastic parameters; a semi-supervised learning neural network model based on an attention mechanism is created, a non-linear mapping relation between the rock core lithogenous phase and the sensitive seismic elastic parameters is established through semi-supervised learning, and a lithogenous phase comprehensive index data body is obtained; and carrying out phase and slice processing on the diagenesis phase comprehensive index data body, screening typical stratigraphic slices, and explaining seismic diagenesis phase space distribution.

The invention belongs to the technical field of sedimentology and artificial intelligence crossing, and particularly relates to a sediment source tracking method based on multi-dimensional morphological characteristics of heavy minerals. According to the method, a'high roundness-fresh fracture 'combined judgment mechanism is established, the key geological evidence of'a fresh fracture surface overlapped on the surface of highly rounded particles' is accurately captured through a double-headed neural network, a standard geological fingerprint logic library is established, and the neural network is combined to train standardized'multi-cycle 'heavy mineral characteristics, so that the high roundness-fresh fracture is obtained. Therefore, under the condition of not depending on expensive geochemical testing, the extraction depth of the mineral morphological characteristics is improved, the ancient sedimentary rock re-cycle component and the far-source direct carrying component are effectively distinguished, the sedimentary dynamics quantitative material source tracking based on the population statistics law is realized, and the method is suitable for mass production. And a more objective, macroscopic and repeatable quantitative data support is provided for basin analysis, paleogeography reconstruction and oil and gas reservoir prediction.

The present application belongs to the technical field of oil exploration, and particularly relates to a method for well-seismic comprehensive fine delineation of sandy conglomerate. The method comprises the following steps: combining with a reservoir configuration division method, fine delineating a vertical sedimentary structure level of the sandy conglomerate; using a seismic sedimentology analysis method, well-seismic comprehensive delineating a spatial distribution of the sandy conglomerate, and clearly defining a spatial feature of a reservoir configuration unit in a target area; establishing a parameter knowledge base of an internal structure unit of the sandy conglomerate, and predicting a favorable reservoir development position in the target area. The method combines geological, logging and seismic fine delineation methods, finds an effective combination point, and applies the combined system method to a sandy conglomerate reservoir for verification, thereby establishing an effective well-seismic comprehensive fine delineation method of the sandy conglomerate. The method effectively overcomes the problems of complex internal structure of the sandy conglomerate and unclear marks.

The invention discloses a deposition path process fingerprint decoding method and system based on a complex source-sink system, and relates to the technical field of tectonic sedimentology and data modeling crossing. Target strata geochemical characteristic data of a research area are collected and arranged, and a large deposition geochemical data set of the research area is established; and establishing standard stratum characteristics and sedimentary geochemical characteristics thereof in combination with a typical stratum profile of the basin. According to the method, the multi-source supply and re-cycle contribution proportion on the geologic source-sink system deposition path is quantitatively recovered through establishment of target layer series multi-dimensional scaling analysis, homogenization degree analysis, fingerprint decoding parameter PCA data statistics and the like, the defects that a traditional source-sink analysis method depends on prior end member selection and is easily influenced by personal errors are overcome, and the method is suitable for large-scale analysis of the geologic source-sink system deposition path. The inverse Monte Carlo model is utilized to carry out iterative simulation for multiple times, the simulation effect is gradually optimized, the accuracy and reliability of the deposition path and process description result are ensured, and a more scientific and accurate technical means is provided for geological research and resource exploration.

The invention discloses a comparative sedimentology-based material source discrimination method, and relates to the technical field of petroleum and natural gas exploration and development. According to the method, the advantage of full coverage of three-dimensional seismic data on an underground sand body is fully utilized, a sedimentary facies type is determined by depicting a sand body plane distribution form and combining a logging facies mark, comparison is carried out by taking a corresponding modern sedimentary environment as a reference, and the source of the river-delta sedimentary system is comprehensively judged by adopting a comparative sedimentology method. According to the method, the source of the sand body can be identified more quickly and accurately, and geological researchers can be helped to more intuitively understand the superposition relationship of the sand bodies in different underground material source directions. The method is particularly suitable for continental basins which are covered by three-dimensional seismic data and are in the atrophy stage, and is expected to be applied to paleogeography reconstruction of shallow water and complex materials and oil-gas exploration and development.

The present application belongs to the technical field of the cross of sedimentology and artificial intelligence, and particularly relates to a sediment source tracking method based on multi-dimensional morphological characteristics of heavy minerals. The present application establishes a "high roundness-fresh fracture" combined discrimination mechanism, accurately captures the key geological evidence of "highly round particle surface superimposed fresh fracture surface" through a double-head neural network, establishes a standard geological fingerprint logic library, and combines neural network training standardization "multi-cycle" heavy mineral characteristics, so as to improve the extraction depth of mineral morphological characteristics without relying on expensive geochemical tests, effectively distinguish the old sedimentary rock re-circulation components and the far-source direct transport components, realize the quantitative source tracking of sediment dynamics based on the population statistical law, and provide more objective, macroscopic and repeatable quantitative data support for basin analysis, paleogeographic reconstruction and oil and gas reservoir prediction.

This invention belongs to the field of geological sedimentology, specifically a method for identifying high-quality lithofacies of storm formation based on multi-source data integration. A: Data Acquisition and Preprocessing; S1: Data Acquisition and Confirmation, i.e., using a convolutional neural network (CNN) to automatically identify "Bouma-like sequence" features in the core based on core samples, outcrops, well logging curves, and well logging data, outputting storm depositional sample information; S2: Using deep learning (U-Net) combined with sedimentary structures to automatically classify lithofacies in the core images; S3: Using time series analysis (LSTM or Transformer) to automatically identify "box-shaped" (storm dam body) and "bell-shaped, finger-shaped" (storm dam side edge) curve features. This invention establishes a high-quality lithofacies identification model by integrating multi-source data, greatly improving the accuracy and efficiency of identification, and providing a scientific basis for evaluating oil and gas reservoirs in storm depositional environments.

The present application relates to the field of sedimentology, in particular to a method for analyzing deep-time El Nino-Southern Oscillation cycle based on annual lamination of marine and lacustrine sediments. Through sampling, grinding, thin sectioning, high-definition scanning, estimating sedimentation rate, element plane distribution, principal component analysis of each element content, laser in-situ carbon and oxygen isotope testing of each annual lamination, and time domain spectrum analysis, the climate change of interannual scale in geological history period is finally analyzed. The present application combines the analysis and interpretation of element data, annual lamination thickness and annual lamination carbon and oxygen isotopes, which shows that there is correlation between each index, provides a new method for identifying and analyzing deep-time El Nino-Southern Oscillation cycle, establishes a time scale with years as a unit based on marine or lacustrine annual lamination, and carries out high-precision analysis on climate fluctuation under this scale, which fills the gap in the related art.

The invention relates to a fluvial facies reservoir three-dimensional space feature prediction method based on single well data. The method comprises the steps that S1, charts of different types of fluvial facies reservoirs are constructed based on the sedimentology principle; s2, according to parameter changes of different types of fluvial facies reservoirs, determining a three-dimensional space characteristic change mode of a plate corresponding to each type of fluvial facies reservoir; s3, according to the charts and the three-dimensional space characteristic change modes of the fluvial facies reservoirs of different types, constructing an analogy index system; s4, new drilling well information is obtained, drilling well related indexes in the new drilling well information are extracted, and an evaluation index sample of a new drilling well is constructed; s5, compiling a fluvial facies reservoir feature sample table and a reservoir feature three-dimensional space change diagram according to the evaluation index sample of the new drilled well and the analogy index system of the different types of fluvial facies reservoirs in combination with the charts and the three-dimensional space feature change modes of the different types of fluvial facies reservoirs, and predicting the three-dimensional space feature change of the reservoir at the new drilled position.

The invention discloses a quantitative seismic sedimentology method, which relates to the technical field of oil-gas exploration, and comprises the following steps of: establishing a nonlinear learning model of logging curves (Vp, Vs and RHOB) and a gamma curve (GR) by adopting a random forest learning method, and then applying the model to a three-dimensional elastic parameter body (Vp, Vs and RHOB) to obtain a three-dimensional gamma (GR) body; a three-dimensional gamma (GR) body with clear geological significance is obtained through a data driving mode, and the problem of obtaining a three-dimensional geological parameter body (GR) through three-dimensional seismic data is solved; secondly, replacing a three-dimensional seismic data volume adopted by the existing method with a three-dimensional gamma (GR) data volume in the process of seismic sedimentology analysis and stratigraphic slice analysis, and realizing quantitative seismic sedimentology interpretation under the constraint of a lithology quantitative interpretation standard; and finally, since the random forest algorithm carries out sampling every time to train the model, the generalization ability is very strong, and the method plays a role in reducing the variance of the model.

The invention relates to the cross technical field of geochronology and sedimentology, and discloses a laser in-situ isotope measurement method for limiting the deposition age of sandstone containing carbonate cement, which comprises the following steps: S1, sample collection and preparation: carrying out sample collection and sample separation on a sandstone sample containing carbonate cement and chipping zircon; s2, selecting particles in a to-be-detected area, performing period analysis on the carbonate cement, selecting the to-be-detected area, and selecting particles meeting conditions from the chippings of zircon; step S3; u-Pb isotope analysis, wherein an LA-ICP-MS device is adopted for conducting single-point laser denudation on the to-be-detected area and the particles, and isotope analysis is conducted; s4, data processing and age explanation are conducted, the age of carbonate cement is obtained to serve as the lower limit of sandstone deposition age, and the age of the young group of chipping zircon is obtained to serve as the upper limit of sandstone deposition age; and S5, comprehensively limiting the deposition age, and constraining the real deposition age of the sandstone in combination with the upper limit and the lower limit.

The invention discloses a thin stratum thickness prediction method and system based on a cause method and a medium, and relates to the technical field of stratum thickness prediction, and the method comprises the steps: S10, analyzing stratum distribution main control factors; s20, constructing boundary description; s30, performing qualitative recovery on the stratigraphic geologic model in combination with a stratigraphic distribution rule and an earthquake depicted structural boundary to obtain a stratigraphic thickness trend model; s40, establishing a stratum thickness prediction data model in combination with the drilled stratum thickness data and the stratum thickness trend model, and performing iteration on the stratum thickness prediction data model; s50, according to the actual drilling well deviation, the well bottom coordinate of the top of the target stratum is calculated; and S60, key parameters of the well bottom and the coordinate boundary are calculated, and then the key parameters are input into the stratum thickness prediction data model to predict the stratum thickness. The stratum thickness is predicted by introducing a sedimentology principle and a cause method, the logicality is high, and the method is more reasonable; meanwhile, the requirement for seismic data quality is lowered, compatibility is high, and seismic data are prevented from being over-processed.

The invention discloses a sequence stratigraphy and seismic sedimentology-based fine characterization method for a braided channel reservoir. The method comprises the following steps of: obtaining core, logging and high-resolution three-dimensional seismic data of a target area; determining sedimentary facies types and sedimentary microfacies division by analyzing and researching sedimentary signs and logging facies signs in the target area; the method comprises the following steps: identifying the type and characteristics of a sequence interface by utilizing lithology and a logging curve and applying sedimentology and high-resolution sequence stratigraphy theories, and carrying out high-resolution sequence stratigraphic division; dividing a reservoir configuration interface; and according to the internal structure of the sedimentary system, the sedimentary facies distribution, the focused high-frequency sequence and the sedimentary body scale, generating a fine characterization result of the braided river reservoir of the target area configuration unit. According to the method, the defects in the aspects of reservoir fine description, boundary identification, parameter inversion and the like in the prior art are effectively overcome, and the efficiency and success rate of oil and gas reservoir development are improved.

The application discloses a carbonate reservoir prediction method and system based on seismic sedimentology, and comprises the following steps: collecting data of a research area; identifying and dividing sequence interfaces of a target layer by analyzing seismic data and well logging curves; evaluating seismic amplitude data of the target layer and determining an effective signal range; performing frequency division processing on the seismic data in the effective signal range; performing high-precision sequence interface seismic tracking interpretation; analyzing distribution rules of reservoirs and distribution conditions of special lithology in a sequence framework; establishing a seismic forward geology model; performing seismic forward to confirm seismic response characteristics of the reservoirs and the special lithology; selecting appropriate seismic reservoir prediction methods or technologies for reservoirs at different sequence positions to perform reservoir prediction work; and evaluating reservoir prediction results and removing systematic errors leading to abnormal areas. The application has the advantages of improving the precision and accuracy of reservoir prediction, improving the efficiency and economic benefits of data processing and exploration and development.