19results about How to "Reduce exploration risk" patented technology

The invention relates to a mineralization prediction method for synthesizing rare data based on a mineralization space structure. The method comprises the following steps: collecting and preprocessing geological data based on a prediction demand; based on the geological background of the to-be-studied area, constructing a metallogenic prediction model, determining a prediction variable and creating a prediction layer; performing mineralization space structure analysis, synthesizing a preset number of prediction variables of rare data by combining the prediction variables of the to-be-studied area, and further generating a plurality of training data sets; adopting a plurality of training data sets to respectively train the constructed metallogenic prediction model so as to predict the metallogenic probability of the to-be-researched area; and performing performance evaluation on the metallogenic prediction model, and delineating a prospecting target region based on a prediction result of the metallogenic prediction model with relatively good performance. According to the metallogenic prediction method disclosed by the invention, the nuclear density analysis is carried out on the space distribution of the known ore deposit, so that a certain number of positive samples can be synthesized under the condition of considering the geological connotation, the training of the metallogenic prediction model is facilitated, and the prospecting prediction precision is improved.

The invention provides a mud-phase shale component quantitative analysis and calculation method, which relates to the technical field of geological mineral exploration and mainly comprises the steps of sample collection and pretreatment, X-ray diffraction pattern acquisition, pattern analysis and mineral component determination, quantitative calculation based on an innovative model, result correction and evaluation and the like. Through an innovative quantitative analysis model and a strict sample processing flow, the content of various mineral components in the mud-phase shale can be more accurately determined, analysis errors are reduced, more reliable data support is provided for geological research and resource exploration, and compared with a traditional method, the method has the advantages that operation steps are simplified, analysis efficiency is improved, and the method is suitable for popularization and application. According to the method, the quantitative analysis of the mud-phase shale components can be completed in a shorter time, the progress of research and exploration is accelerated, the dependence on complex and expensive chemical analysis equipment is reduced, the experiment cost and the human input are reduced, and meanwhile the utilization efficiency of resources is improved.

The invention relates to a reservoir physical property parameter prediction and evaluation system for oil-gas exploration, and aims to deal with exploration challenges under complex geological conditions. The system comprises a multi-source data acquisition module, a self-adaptive data processing module, a self-learning prediction module, a dynamic model correction module and a visual result display module. By integrating logging, core analysis, earthquake and historical exploration data, the system performs high-precision prediction on the porosity and permeability of the reservoir, and updates the model in real time through a dynamic correction technology to ensure the accuracy and reliability of a prediction result. In addition, the system also provides two-dimensional and three-dimensional visual display and uncertainty analysis functions, and assists a decision maker in identifying a high-potential reservoir area and making an optimal development scheme. The system has the interface connection capability with an external data management system, can realize automatic updating and economic analysis of data, and effectively improves the efficiency and success rate of oil-gas exploration.

The present invention discloses a method for optimizing the configuration of controlled source electromagnetic exploration data acquisition parameters, comprising the following steps: step 1, using the staggered grid finite difference method to complete the numerical calculation of the three-dimensional electromagnetic response of the controlled source electromagnetic exploration and analyze the electromagnetic response characteristics; step 2, using the Euclidean distance and the first-order difference of the singular value spectrum of the sensitivity matrix to construct the quality evaluation function; step 3, establishing the objective function, using the multi-objective non-dominated sorting genetic algorithm to search the Pareto optimal solution set; step 4, according to the actual problem, extracting the optimal transmission frequency and the data acquisition parameters such as the receiving and transmitting distance from the Pareto optimal solution. The present invention is helpful to improve the resolution of the observed data to the target body, improve the reliability of the model parameter estimation; can control the exploration cost, reduce the exploration risk; and deepen the understanding of the correlation between the data acquisition parameters and the electromagnetic properties of the geological target body.

This invention discloses a method, apparatus, equipment, and medium for oil and gas resource evaluation based on ensemble learning. The method includes acquiring reservoir sample data; preprocessing the reservoir sample data to obtain target reservoir sample data; constructing an oil and gas resource evaluation model based on the target reservoir sample data; and evaluating oil and gas resources based on the oil and gas resource evaluation model; wherein the oil and gas resource evaluation model is an oil and gas resource evaluation model based on ensemble learning. This technical solution, by constructing an oil and gas resource evaluation model using an ensemble learning method, can achieve refined prediction of oil and gas resource distribution, reduce exploration risks, improve resource utilization efficiency, and provide scientific decision support for oil and gas exploration and development.

The invention provides a deep learning identification method and device for a dry layer and an effective reservoir based on four parameters, and the method comprises the steps: finding out one or more representative wells from a research region, and carrying out the interpretation of a layer section of a research target layer; digitalizing the lithology interpretation result of the target stratum; analyzing the logging data of the well in the research area, and normalizing the logging data of the representative well; constructing a deep learning network structure with an input layer with input neurons and bias neurons, a hidden layer and a neuron output layer by using a TensorFlow framework; setting a learning rate of a deep learning network gradient descent algorithm; setting a cost function of the deep learning network as a square error cost function; setting training times, and training the deep learning network on the basis of the training sample to obtain deep learning network parameters; and testing the training sample and the test sample by using the obtained deep learning network parameters. According to the method, the dry reservoir and the effective reservoir can be effectively distinguished from the complex and diversified reservoirs.

The invention discloses a method for predicting under-compaction low-speed mudstone distribution based on geometric factor parameters, and belongs to the field of geological engineering and exploration. An LSTM multilayer long short-term memory network structure is set, and training is performed through a preset training set and a preset test set; iteratively calculating the incidence relation of the target function and the geometric factor data set in the time dimension by adopting a gradient updating algorithm of time truncation back propagation, completing the training of the LSTM network, and obtaining a geometric factor prediction model; and evaluating the performance of the model by using a mean absolute error MAE and a root-mean-square error RMS, and finally predicting a geometric factor according to the LSTM network obtained through optimization and determining the distribution range of the under-compacted mudstone and the compaction degree of the under-compacted mudstone. Therefore, the possible development range of the low-speed mudstone is identified, so that the probability of falling into a'bright spot 'trap is reduced before exploration and development.

The present invention provides a method for prospecting a copper polymetallic ore target area based on aeromagnetic data, and relates to the technical field of mineral resource prospecting. The method performs variable-angle polarization on the acquired aeromagnetic data to obtain aeromagnetic ΔT anomalies under vertical magnetization; the aeromagnetic ΔT anomalies after polarization are extended upward to obtain regional anomalies and remove them, and obtain aeromagnetic local anomalies; the aeromagnetic data is subjected to depth-weighted three-dimensional magnetic susceptibility structure inversion, and the optimal solution of the objective function is solved to obtain the three-dimensional distribution structure of the crustal magnetic susceptibility; the aeromagnetic ΔT anomalies after polarization are subjected to depth inversion based on power spectrum analysis to obtain the Curie point depth of the study area; the three-dimensional fracture information of the three-dimensional magnetic susceptibility structure is extracted to obtain the three-dimensional fracture distribution in the crust; and the above information is comprehensively analyzed to establish the optimization principle of the favorable area of ​​copper polymetallic ore. The present invention provides support for the exploration and development of copper polymetallic ore based on high-precision aeromagnetic data and comprehensive multiple physical property parameters.

The embodiment of the present application provides a method, device, electronic device and storage medium for evaluating the scale of porphyry copper deposits, and relates to the field of ore deposit scale exploration. Rock samples are collected from the area to be explored, and zircon monominerals are sorted out from the rock samples; elemental analysis is performed on the zircon monominerals and rock samples, and an element database is established; the element database is screened according to preset indicators to obtain target element data; the ore deposit scale index of the area to be explored is calculated based on the target element data, wherein the ore deposit scale index includes the V / Sc of the rock and the Eu / Eu* of the zircon; the porphyry copper deposit scale of the area to be explored is evaluated according to the ore deposit scale index, and representative ore-forming rock body data can be obtained by substituting the ore deposit scale index into the ore deposit scale index to more accurately evaluate the porphyry copper deposit mineralization potential and scale, thereby reducing the input cost, improving the exploration efficiency and reducing the exploration risk.

The invention belongs to the field of sandstone type uranium mine resource geophysical exploration and development, and discloses an iterative optimization method for improving the signal-to-noise ratio of sandstone type uranium mine extremely-shallow layer seismic data, which firmly grasps the problem essence of the extremely-shallow layer seismic data of a sandstone type uranium mine reservoir, and adopts an iterative signal-to-noise ratio improving method to improve the signal-to-noise ratio of the extremely-shallow layer seismic data of the sandstone type uranium mine reservoir. On this basis, data regularization based on Fourier reconstruction is also performed for the problems of low coverage frequency and irregular data caused by the influence of earth surface factors and acquisition factors on the extremely shallow seismic data, and the method has very good theoretical technical progress and is suitable for popularization and application. The problems of low signal-to-noise ratio, low coverage frequency and irregular data of the ultra-shallow seismic data of the sandstone type uranium mine are fully solved.

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 invention discloses a multi-element exploration and prediction method for hydrothermal cobalt ore. The method comprises the following steps: firstly, under the guidance of a regional metallogenic theory, delineating a cobalt ore prospecting prospecting area and a prospecting target area by integrating geological, geophysical prospecting and chemical prospecting data; secondly, performing large-scale geological mapping and remote sensing interpretation in the prospecting target area to identify structure and alteration marks, and delineating geophysical and geochemical exploration anomalies by using high-precision magnetic survey, induced polarization profiles and soil geochemical profiles; then, aiming at a geophysical and geochemical exploration abnormal part, a lithologic interface and a structural development part, utilizing a scribed line investigation sample to quickly position a mineralization favorable part, and utilizing groove exploration to carry out shallow earth surface control; then, the form, scale and resource potential of the deep ore body are verified through drilling engineering; and finally, establishing a prospecting prediction model by taking the target region with the outstanding prospecting effect as a model region, and guiding peripheral prediction. The method has the advantages of being convenient, environmentally friendly, remarkable in prospecting efficiency and the like, the purpose of rapidly recognizing the ore-bearing position of the cobalt ore is achieved, and a good cobalt ore prospecting effect is achieved in the East Kunlun inflator River region.

The present application belongs to the technical field of geophysical exploration, and relates to a method and system for improving seismic data resolution through frequency extrapolation, comprising: collecting a seismic signal, and representing the seismic signal by a reflection coefficient sequence and a seismic wavelet; fitting the reflection coefficient sequence to obtain a plurality of candidate fitting functions, selecting a function having the least influence on the reflection coefficient sequence from the fitting functions, and calculating an entropy norm of the reflection coefficient sequence according to the function having the least influence on the reflection coefficient sequence; converting the reflection coefficient sequence into a reflection coefficient in a frequency domain, performing frequency extrapolation on the reflection coefficient in the frequency domain through the function having the least influence on the reflection coefficient sequence, and solving an optimization problem through iteration; making the reflection coefficient obtained through the frequency extrapolation satisfy a coherence constraint condition, and outputting the frequency extrapolation reflection coefficient meeting the coherence constraint condition. The present application can effectively improve the seismic data resolution while maintaining the continuity of seismic events.

This invention belongs to the field of seismic data processing technology for petroleum exploration. It discloses a method for processing contiguous seismic data. The method involves coherent enhancement and Fourier transform of seismic traces in seismic data A and B, obtaining the phase and amplitude spectral factors of each trace in the frequency domain. After smoothing, the values ​​are summed and averaged to obtain the phase and amplitude factors in the frequency domain. Then, an inverse Fourier transform is performed to convert the data to the time domain, yielding phase and amplitude shaping operators. These operators are then convolved and sequentially applied to seismic data A to achieve contiguous data shaping from seismic data A to seismic data B. This invention is used for contiguous processing of seismic data and has advantages such as good stability, high accuracy, and strong applicability.

The application discloses a preferred method, device, electronic equipment and storage medium for constructing traps. The method comprises the following steps: determining a plurality of target structural traps of each target stratum corresponding to a target region based on a depth domain construction map of the target region; determining a first velocity corresponding to the target structural trap based on a blind well velocity field constructed by a well calibration average velocity and a root mean square velocity spectrum of a target well and a target stratum of the target structural trap, and determining a second velocity corresponding to the target structural trap based on an optimal velocity field and the target stratum of the target structural trap; predicting a burial depth error of the target structural trap based on the first velocity and the second velocity, determining a comparison result of the target structural trap based on the burial depth error of the target structural trap and a preset threshold, and determining a drilling sequence of the plurality of target structural traps based on the comparison result of each target structural trap. The embodiment solves the problem that the depth error of the structural trap affects the accuracy of the preferred structural trap, and avoids the influence of the velocity field construction error on exploration.

The application discloses a curtain opening system dissolution quantitative evaluation method under a high-temperature closed background, which comprises the following steps: carrying out source partition on a research area, determining mineral composition, interstitial type and pore type of different source areas, and determining compaction intensity sequence of different source areas; reconstructing a time-space sequence of diagenetic fluid activity, establishing a dynamic model of fluid-rock interaction and pore evolution; determining a mineral differential dissolution conversion mechanism; quantifying mineral differential dissolution under a curtain opening / closed system; and based on mineral composition component difference, fluid type and mineral dissolution effect difference, establishing a curtain opening system deep clastic rock reservoir dissolution quantitative evaluation method under a closed background by combining diagenetic mineral quantitative statistics and theoretical calculation. The application realizes quantitative-semi-quantitative prediction of dissolution effect, thereby delimiting a favorable dissolution development area, and effectively improves reservoir prediction precision, provides a scientific basis for drilling deployment, and reduces exploration risk.

The present application relates to a kind of stratum pinch-out line prediction methods based on geology-seismic combination, comprising the following steps: data collection;According to well logging electrical curve and core, select lithology-electricity marker layer, establish lithology-electricity relationship, determine development characteristics and interface identification mark;Establish regional well profile, clear sequence stratigraphic distribution law, construct stratum lateral variation geological model;Based on three-dimensional seismic work area, in combination with regional well profile, draw section stratum pinch-out trend line;Using stratum thickness method, refine pinch-out line position, clear stratum pinch-out line geological characteristics;Using single well stratum thickness and interwell horizontal distance, the included angle of pinch-out trend line at pinch-out point is obtained, average value of included angle;Through included angle extrapolation method, the position of pinch-out line is calculated and determined;Using seismic phase rotation processing method, the pinch-out point is identified and finely described;Based on the position of pinch-out line determined, in combination with the aforementioned analysis results, the distribution range of stratum pinch-out line is comprehensively predicted.

The invention relates to a shale organic matter enrichment main control factor quantitative characterization method and system based on machine learning, and belongs to the technical field of unconventional energy exploration evaluation. According to the method, a shale organic matter enrichment influence factor parameter system covering paleoclimate, paleo-productivity, oxidation-reduction conditions, terrestrial source input and deposition rate is constructed, the index collinearity problem is solved through principal component analysis dimension reduction processing, and a gradient boosting tree model is adopted to establish a nonlinear relation between multi-source geological parameters and organic matter enrichment degree. Systematic analysis is carried out by fusing four types of feature importance evaluation methods including statistical analysis, information theory, model structure and model interpretation, and a comprehensive influence value is converted into a standardized percentage to be output. According to the method, the accuracy and reliability problems of shale organic matter enrichment main control factor identification are effectively solved, conversion from qualitative description to quantitative characterization is realized, and a scientific and efficient quantitative technical support is provided for shale gas resource evaluation and exploration deployment.

The invention discloses a seismic data processing method and device for prestack depth migration, and the method comprises the steps: obtaining the elevation data and micro-logging data of a to-be-processed work area, and respectively building a near-surface velocity model based on a real surface and a near-surface velocity model based on a small surface smooth surface; according to the static correction value of the real earth surface and the static correction value based on the earth surface small smooth surface, determining a residual correction value between the earth surface small smooth surface and the real earth surface; according to the determined residual error correction value, correcting the pre-stack data of the to-be-processed work area based on the real earth surface to first pre-stack data matched with the near-earth surface velocity model based on the earth surface small smooth surface; establishing a full-depth pre-stack depth migration velocity model according to the near-surface velocity model based on the surface small smooth surface, the VSP logging data of the to-be-processed work area and the geologic structure model; and performing depth migration imaging processing on the first pre-stack data by adopting a pre-stack depth migration velocity model to obtain seismic data after pre-stack depth migration.