102 results about "Seismic inversion" patented technology

The invention discloses a multi-well consistency correction method, system and equipment based on Warisstein distance, and relates to the field of oil and gas field exploration and development, and the method comprises the steps: constructing an equal-width histogram of a standard well based on a logging curve of the standard well; according to the equal-width histogram of the standard well and the logging curves of the multiple to-be-corrected wells, on the basis of an empirical distribution function method, with the minimum Warisstein distance between each to-be-corrected well and the standard well as the target, an annealing algorithm is adopted to conduct iterative optimization on parameters of the consistency mapping function, and optimal parameters corresponding to each to-be-corrected well are obtained; inputting a consistency mapping function to obtain an optimal matching correction model corresponding to each well to be corrected; and inputting the logging curve of each to-be-corrected well into the corresponding optimal matching correction model to obtain a corrected logging curve of each to-be-corrected well, and completing multi-well consistency correction. According to the method, the accuracy of the corrected logging curve can be improved, so that a solid data foundation is laid for subsequent seismic inversion and reservoir prediction.

The present application relates to a kind of thin interbed type deposit high-quality reservoir prediction method, comprising: obtaining the geological data, seismic data and well logging data of the region to be analyzed, well seismic combination tracking interpretation oil group top, bottom interface;With the oil group top, bottom interface as constraint, utilize the three-dimensional seismic data acquisition three-dimensional seismic inversion data;With the oil group top, bottom interface as constraint, according to the three-dimensional seismic inversion data and the well logging data are combined, obtain oil group internal relative isochronous sedimentary interface;According to the oil group internal relative isochronous sedimentary interface, based on three-dimensional seismic inversion data, each small layer reservoir plane distribution in oil group is solved;According to the three-dimensional seismic inversion data, based on the information of actual drilling well, sandstone probability body seismic data is solved;Utilize actual drilling well information to construct reservoir classification standard, realize single well reservoir type division;According to the sandstone probability body seismic data, with single well reservoir type as constraint, carry out high-quality reservoir space distribution prediction.

The invention discloses a low-frequency attribute model acquisition method, device and equipment and a readable storage medium, and belongs to the technical field of seismic inversion. The method comprises the following steps: acquiring a three-dimensional geological structure model of a target area, a first attribute value of each sampling point included in an observation well located in the target area and a second attribute value of each seismic node included in a seismic body corresponding to the target area; generating a first sequence small layer model according to the three-dimensional geological structure model, each sampling point and each seismic node; domain transformation is carried out on the first sequence small-layer model to obtain a second sequence small-layer model under the sedimentary domain; according to a first attribute value and a second attribute value of a target node in nodes included in each layer in the second sequence small-layer model, obtaining a layer model corresponding to each layer in the second sequence small-layer model; and generating a low-frequency attribute model according to the second attribute value of each node included in the second layer sequence small-layer model and the layer model corresponding to the layer where each node is located. And the precision of the low-frequency attribute model is improved.

The invention discloses a carbonate rock pore type prediction method and device based on deep learning, and the method comprises the steps: obtaining training well data, building a sample data set, and constructing a CNN network model; inputting the training sample data set into the CNN network model to obtain a logging interpretation result, and dynamically adjusting parameters of the CNN network model, so that the calculation efficiency and prediction precision of the network model are higher; and obtaining the velocity and density of longitudinal and transverse waves of a test well, inputting the velocity and density into the trained CNN network model for inversion, and obtaining a prediction result of the pore type of the carbonate reservoir of the test well. And in combination with the trained CNN network model and the elastic parameters of the pre-stack seismic inversion, obtaining the spatial distribution characteristics of the multiple pore types. According to the method, data testing and learning network structure optimization are carried out on the basis of logging data in combination with a CNN (convolutional neural network) deep learning network, the learning network structure is applied to elastic parameters of pre-stack seismic inversion, and high-precision and stable quantitative characterization of spatial distribution of multiple pore types of the carbonate reservoir is realized.

The invention discloses a thick sand body thin dessert quantitative description method based on seismic forward iteration driving. The method comprises the steps of determining single well lithofacies division, a reservoir type and a dessert reservoir type; constructing a lithofacies and dessert seismic inversion attribute probability body; constructing a three-dimensional lithofacies model by taking the lithofacies probability body as a trend constraint, and constructing a double-constraint dessert body three-dimensional model by taking the model as phase control and the dessert probability body as a constraint; a wave impedance body is constructed according to a reservoir type statistical wave impedance mean value, a near-angle superposition seismic body is selected as a test seismic body, and seismic wavelets are preferably selected; taking the wave impedance body, the test seismic body and the seismic wavelets as input, generating a forward modeling seismic data body through convolution operation, and calculating a three-dimensional correlation coefficient between the forward modeling seismic data body and the original seismic body; and when the three-dimensional correlation coefficient is greater than a set threshold value, outputting a double-constraint dessert body three-dimensional model, otherwise, correcting geologic model parameters, updating the model, carrying out iterative calculation again until conditions are met, and finally depicting thin dessert distribution in the thick sand body according to the conditions.

The invention relates to the technical field of electrical prospecting, and particularly discloses an electromagnetic temperature calculation method and device for simulating seismic inversion, and the method comprises the steps: obtaining a logging temperature curve based on logging information; the logging temperature curve is preprocessed; establishing a temperature initial model based on the preprocessed logging temperature curve; preprocessing the electromagnetic seismic data, and establishing electromagnetic simulated seismic data based on the preprocessed electromagnetic seismic data; carrying out seismic inversion based on the electromagnetic simulated seismic data and a temperature initial model; and displaying the heat storage body subjected to seismic inversion in a three-dimensional space. According to the invention, by means of the idea and steps of post-stack inversion in actual seismic exploration, seismic inversion is carried out based on electromagnetic simulated seismic data and a temperature initial model; and the heat storage body subjected to seismic inversion is displayed in a three-dimensional space, so that a new thought can be provided for magnetotelluric temperature inversion of a seismic exploration area, and the accuracy and credibility of electromagnetic temperature inversion are greatly improved.

The application discloses a kind of based on deep learning's seismic brine reservoir response feature identification method, by fusing induction logging data and seismic inversion result, break through the technical limitation that brine reservoir feature response is weak to traditional acoustic logging, improve the accuracy of reservoir identification;Improved UNET++ network structure utilizes dense skip connection and channel attention mechanism, effectively integrates multi-scale geological features, enhances the capture ability of thin layer brine weak reflection signal, improves the accuracy of boundary identification;Introduce bidirectional LSTM and spatial feature alignment technology, realize the depth fusion of logging vertical feature and seismic horizontal profile, optimize horizon matching relationship, effectively reduce the risk of misjudgment caused by velocity anomaly;Based on deep supervision mechanism and joint training strategy, generate three-dimensional reservoir spatial distribution model, realize the description of deep pore-fracture type brine reservoir, greatly improve exploration efficiency, provide reliable technical support for brine resource positioning under complex geological conditions.

The invention discloses a crack parameter prediction method under the action of coupling pressure, electronic equipment, a storage medium and a device. The method comprises the following steps: calculating Thomsen anisotropy parameters of a VTI background based on an azimuth seismic trace set and logging interpretation data, and further calculating theoretical fracture parameters; constructing an anisotropic medium seismic inversion target functional based on the filtered anisotropic parameters as constraint conditions, and performing optimization solution on the target functional based on theoretical fracture parameters as priori knowledge to obtain optimal fracture parameters; and constructing an HTI medium azimuth amplitude difference inversion target functional under the action of coupling pressure, carrying out optimization solution on the target functional by taking the optimal fracture parameter as priori knowledge, and predicting the fracture parameter. According to the method, the anisotropic parameters and the effective pressure sensitive parameters are stably, reasonably and directly obtained from the five-dimensional seismic data, so that the problem of unstable inversion of the anisotropic parameters caused by poor quality of the seismic data is solved, and stable prediction of the fracture parameters is realized.

The application discloses a horizontal well geosteering method, comprising the following steps: acquiring basic data of adjacent wells around a to-be-drilled horizontal well; comparing target reservoirs to obtain the numerical values of the top depth and the bottom depth of the target reservoirs of the adjacent wells and the predicted values of the top depth and the bottom depth of the to-be-drilled horizontal well; comparing X-ray element data to predict the sandstone types of the target reservoirs and the reaction characteristics of the X-ray element data; obtaining the reservoir thicknesses of each adjacent well and the to-be-drilled horizontal well, and establishing a target reservoir thickness model; when the to-be-drilled horizontal well is under construction, drilling to a target point, analyzing and judging whether the actual sandstone type of a drilled formation is the predicted sandstone type, analyzing whether the actual sandstone type is consistent with the predicted value, and when the results are all yes, determining that the model is correct, and continuing to perform horizontal well geosteering. The method can timely correct the prediction result of a seismic inversion reservoir, provide a basis for trajectory adjustment of horizontal well construction, improve the drilling rate of the reservoir, and improve the success rate of trajectory adjustment.

This invention discloses a seismic inversion method for inclined fracture parameters and brittleness indicator factors, comprising the following steps: (1) calculating Young's modulus and brittleness indicator factors; calculating two inclined fracture parameters using fracture density and fracture dip angle; performing interpolation and extrapolation based on seismic stratigraphic data and well logging data to obtain an initial model of Young's modulus, brittleness indicator factors, density, fracture density, and two inclined fracture parameters; (2) extracting azimuth angle seismic wavelets; (3) combining azimuth partial angle superimposed seismic data, azimuth angle seismic wavelets, and the initial model, obtaining Young's modulus, brittleness indicator factors, density, fracture density, and two inclined fracture parameters through Bayesian inversion; calculating the fracture dip angle based on the inverted two inclined fracture parameters. This invention can provide stable and reliable prediction results for brittleness indicator factors, fracture density, and fracture dip angle, which is helpful for conducting seismic characterization of brittleness and inclined fractures in shale gas reservoirs.

The invention relates to a full-band high-precision seismic inversion method, which belongs to the technical field of reservoir prediction in oil and gas field exploration, and comprises the following steps: firstly, carrying out low and medium frequency filtering interpolation on logging data to establish a wave impedance initial model, and then carrying out wave impedance inversion based on seismic data; and carrying out waveform clustering on residual seismic disturbance obtained by subtracting actual seismic data from forward modeling data of the wave impedance inversion result, carrying out transverse high-frequency inversion on a logging high-frequency component according to a waveform clustering result, and finally fusing a low and medium frequency inversion result and a high-frequency inversion result to obtain a full-band high-precision inversion result. According to the method, the high-frequency component inversion is driven by the residual seismic disturbance obtained by subtracting the actual seismic data from the forward modeling data of the low and medium frequency inversion result, so that the difficulty of inaccurate high-frequency information inversion of the existing inversion method is effectively overcome, the full-band high-precision inversion driven by the seismic data is realized, the resolution and accuracy of the seismic inversion are greatly improved, and the method is suitable for large-scale popularization and application. And accurate identification of the thin reservoir in oil and gas field exploration and development is facilitated.

The invention discloses a gas reservoir three-dimensional geological modeling method and system and a medium. The method comprises the following steps: acquiring multi-source data of a target work area; in combination with vertical seismic section data and virtual well data, depth correction is performed on the horizon after time-depth conversion, and a three-dimensional tectonic framework model is established based on the corrected horizon and fault data; fusing and generating a sand body space development probability trend body based on the seismic attribute body, the seismic inversion body and the determined optimal weight coefficients of the seismic attribute body and the seismic inversion body; a three-dimensional tectonic framework model is used as a space framework, a sand body space development probability trend body is used as a transverse soft constraint, a plurality of isoprobable lithofacies model initial implementation is established, and a three-dimensional lithofacies model is obtained; a three-dimensional distribution model of reservoir physical property parameters is generated; and importing the three-dimensional lithofacies model and the three-dimensional distribution model into a numerical simulator, performing historical fitting by using gas reservoir dynamic production data, analyzing fitting differences, and performing iterative modeling to obtain a high-precision and low-uncertainty three-dimensional gas reservoir geologic model.

The application provides a kind of intelligent seismic inversion prediction method based on deep learning, comprising: building depth convolution multi-attribute multi-scale hyper-asymmetric Cycle-JNET network model;Obtain data set, i.e. all kinds of well seismic data, divide well curve data and seismic data in well seismic data body into the required 1*1*128 size respectively, each well curve data has corresponding seismic data;Wavelet transform the model data in the training data set, get well seismic data of different scales, each well curve data has corresponding seismic data, well seismic data is input into the multi-attribute multi-scale hyper-asymmetric Cycle-JNET network model for training, and get trained JNET model;Seismic inversion body construction is carried out by the method of writing in each trace.The application realizes the corresponding relationship of well seismic data by learning, can obtain high resolution, high accuracy seismic inversion body through seismic data, improves the accuracy of reservoir identification and prediction work, guides the development of work area and well arrangement work.

The application discloses a kind of three-dimensional seismic forward modeling methods and systems for carbonate karst cave reservoir, belong to the field of oil and gas exploration seismic forward modeling.The method of fusing the three-dimensional P-wave velocity body of the carved-out karst cave reservoir quantitatively into seismic interval velocity establishes three-dimensional seismic forward modeling P-wave velocity model for karst cave reservoir, and the method of carrying out seismic forward modeling based on the model;In the process of three-dimensional seismic forward modeling for carbonate karst cave reservoir, the seismic inversion P-wave impedance body after carving by well logging petrophysical analysis is introduced into the process of establishing P-wave velocity model for the first time, so that the established karst model is more in line with geological understanding and actual situation, and the established seismic forward modeling P-wave velocity model is more accurate.The three-dimensional seismic forward modeling P-wave velocity model of carbonate karst cave reservoir obtained by the application can more truly represent the shape and size of actual carbonate karst cave reservoir compared with the model made by other traditional methods.

The invention relates to the field of oil and gas resource exploration, and discloses a fracturing design method based on shale content three-dimensional geological modeling, which comprises the following steps: determining geological characteristics of a research area, integrating earthquake, logging and core data, analyzing reservoir deposition characteristics and shale content distribution, and combining porosity and permeability to establish a correlation model of a reservoir and shale content; the method comprises the following steps: establishing a shale content three-dimensional geologic model by adopting a sequential Gaussian simulation method by taking well data as hard data and seismic inversion data as soft data through petri l software, constructing a reservoir three-dimensional geologic model according to porosity, permeability and reservoir thickness, and fitting the shale content three-dimensional geologic model and the reservoir three-dimensional geologic model through a variation function to obtain a compressible reservoir model. By combining microseismic monitoring, acoustic logging and a pressure sensor, generation and extension of the crack and reservoir pressure change can be tracked in real time, it is ensured that the crack is effectively extended according to a design path, and a real-time feedback system provides accurate data support in the crack extension process.

The present application relates to a kind of multi-information fusion phased low-frequency modeling method, device and storage medium.The method is drilled well logging data and measured elastic parameter curve, fitting out the fitting relationship of logging longitudinal wave velocity curve and other elastic parameter curve.According to fitting relationship, seismic velocity is converted into low-frequency elastic parameter body.Seismic velocity is the longitudinal wave velocity obtained by processing seismic data.From low-frequency elastic parameter body, the elastic parameter pseudo well curve of drilled well is extracted, and according to the elastic parameter pseudo well curve of drilled well and measured elastic parameter curve, the relative elastic parameter curve of depth-trend is calculated.Based on fault, horizon structure framework and relative elastic parameter curve, relative elastic parameter model is obtained.Low-frequency elastic parameter body is phased backfilled into relative elastic parameter model to obtain multi-information fusion phased low-frequency model.The present application can improve the precision of low-frequency modeling, and then improve the accuracy and reliability of subsequent seismic inversion reservoir prediction result.

This invention discloses a tectonically constrained depth-domain multichannel collaborative seismic inversion method and system. First, a depth-domain wavelet matrix is ​​constructed and combined with an impedance model to generate synthetic records consistent with actual seismic characteristics, thus building a pre-training dataset. Gaussian noise is superimposed on the synthetic data to simulate stratigraphic displacement. Then, plane wave decomposition is used to extract tectonic dip information from the enhanced data, establishing lateral spatial constraints. Tectonic dip constraint terms are embedded in a spatiotemporal neural network, and a multichannel collaborative inversion model is trained using synthetic data. Dip data from real seismic records is extracted and fused with well logging and stratigraphic interpretation results to establish a depth-domain low-frequency initial impedance model. Finally, by combining measured seismic data, tectonic constraint information, the initial model, and well data, network parameters are adjusted through transfer learning, ultimately achieving high-precision and high-stability impedance inversion. This method solves the problems of insufficient result stability, low resolution, and error accumulation effects in existing seismic inversion methods.

This invention relates to the field of oil and gas exploration and development, and discloses a reservoir prediction method, device, storage medium, and processor. The prediction method includes: performing multiple rounds of well-seismic calibration on development wells in a 3D seismic testing area; selecting development wells with a well-seismic correlation coefficient greater than a preset value from the development wells corresponding to the last round of well-seismic calibration as target wells; determining target comprehensive wavelet based on each target well; performing seismic inversion on the reservoir based on the target comprehensive wavelet and preset quality control requirements; predicting the planar distribution of the main sand zone in the reservoir using the seismic inversion results; determining the local thickness zone of the sand body based on the planar distribution of the main sand zone; and characterizing individual sand bodies in the reservoir based on the inversion attributes and the local thickness zone of the sand body. By increasing the constraint of dense well network of development wells, performing multiple rounds of well-seismic calibration, and conducting strict quality control during the seismic inversion process, the accuracy of reservoir prediction can be improved, thereby improving the prediction accuracy of individual sand bodies and facilitating the identification of effective individual sand bodies.

The invention discloses a deep sea land slope well site seabed three-dimensional stability quantitative evaluation method. Comprising the following steps: 1) according to topographic data of a well site area, constructing a seabed three-dimensional finite element discrete model of the well site area; 2) according to the three-dimensional seismic inversion, acoustics and soil mechanics relation model, estimating a three-dimensional space distribution rule of deep sea seabed soil mechanics parameters, and establishing a deep sea three-dimensional seabed slope heterogeneous material model; (3) building a three-dimensional seabed slope stability statics model by considering the accumulation effect of rock debris returned from a well mouth of the drilling well; and 4) calculating the stability of the seabed three-dimensional slope in the well site area by adopting a finite element method, and preferably selecting an optimal drilling well position. The method has the advantages that many induction factors are considered, and the stratum heterogeneity can be reflected more effectively. The method is suitable for deep-sea seabed stability evaluation.

This application relates to the field of shale oil and gas, and discloses a method for identifying sweet spots in shale oil and gas reservoirs, including the following steps: acquiring core CT images, 3D seismic data, and production dynamic data and performing cross-scale preprocessing; constructing a fractional-order nonlocal seepage field model to characterize flow characteristics from nanometer to kilometer scales; predicting seepage field parameters through a Lie group symmetry-constrained neural network; establishing a cross-scale coupling model of quantum adsorption effect and macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and performing multi-objective collaborative optimization to generate a 3D distribution of sweet spots and a development plan. This invention employs cross-scale data fusion technology with fractal dimension dynamic constraints, achieving accurate mapping between nanopore and macroscopic fracture network parameters. Through pore connectivity fractal analysis, it solves the problem of spatial registration misalignment between CT scan and seismic inversion data.

The invention provides a pre-stack seismic inversion method, device, equipment and product based on multi-source data, and the method comprises the steps: obtaining target multi-source data of an inversion research region, the target multi-source data is obtained according to the preprocessed initial multi-source data, and the target multi-source data comprises a logging elastic parameter trend, seismic attributes, geological prior information and horizon constraints; according to a Bayesian hierarchical modeling algorithm, hierarchical fusion is carried out on the target multi-source data, a non-stationary low-frequency model of elastic parameters is constructed, and statistical characteristics of the non-stationary low-frequency model change spatially according to horizon constraints; according to the non-stationary low-frequency model, generating an elastic parameter change trend matched with the inversion research area; and inputting the elastic parameter change trend into a Bayesian inversion framework to complete pre-stack seismic inversion, thereby improving reservoir prediction precision and reliability under complex geological conditions.

The present disclosure provides a horizontal uniaxial stress seismic inversion method based on VTI medium, relates to the technical field of seismic reservoir identification, and comprises the following steps: constructing an effective stiffness tensor of VTI medium under the action of horizontal uniaxial stress; deducing a PP wave reflection coefficient equation of VTI medium under the action of horizontal uniaxial stress; estimating two crack weakness parameters of a stress-free VTI background medium and two anisotropy parameters induced by horizontal uniaxial stress based on an AVOA inversion method of azimuthal Fourier coefficients. The amplitude-migration and azimuth inversion (AVOA) method based on azimuthal Fourier coefficients is applied to actual data, and it is proved that the stress-induced anisotropy model is suitable for VTI medium reservoir prediction with the action of horizontal uniaxial stress.

The invention relates to the technical field of oil exploration and development, and discloses a stratum collapse pressure earthquake prediction method and device based on rock mechanics nonlinear variation, and the method comprises the steps: carrying out the nonlinear fitting of compressive strength with rock cohesion and an internal friction angle through a triaxial mechanics experiment, and obtaining a corresponding fitting relation; then, through seismic inversion, a rock mechanical attribute body, an overlying stratum pressure body, a pore pressure body, a maximum horizontal principal stress body, a minimum horizontal principal stress body, and three-dimensional space rock cohesion distribution and internal friction angle distribution are obtained through calculation; and finally, according to the pore pressure body, the maximum horizontal principal stress body, the minimum horizontal principal stress body, the three-dimensional space rock cohesion distribution and the three-dimensional space rock internal friction angle distribution, predicting stratum three-dimensional collapse pressure body data in combination with a collapse pressure calculation model. By means of the method, the formation collapse pressure can be predicted more accurately, geological disasters caused by improper drilling operation can be reduced, and therefore the geological environment is protected, and potential influences on the earth surface and underground water resources are reduced.

The invention relates to the technical field of seismic data interpretation, and particularly discloses a virtual well construction method and device based on seismic data mining, and the method comprises the steps: constructing a seismic reflection mapping network through employing a reflection coefficient of a known well and a seismic channel beside the well; calculating an initial virtual well reflection coefficient by using the seismic reflection mapping network; based on the initial virtual well reflection coefficient, solving a target virtual well reflection coefficient through a space matching operator; and based on the target virtual well reflection coefficient, calculating through an impedance reflection coefficient formula to obtain target virtual well impedance. According to the virtual well construction method based on seismic data mining, an initial virtual well reflection coefficient is solved by using a seismic reflection mapping network, a target virtual well reflection coefficient is solved through a space matching operator, and finally, target virtual well impedance is calculated according to an impedance reflection coefficient formula. The problem that the seismic inversion precision is finally affected due to low interpolation modeling precision caused by non-uniformly distributed logging data is solved.

The invention relates to a river channel sand body three-dimensional modeling method under seismic inversion control, and the method comprises the steps: determining the three-dimensional space distribution of a target block sand body through geostatistics inversion, distinguishing the river channel sand body in the sand body based on the three-dimensional space distribution of the target block sand body through the data of a dense well network, and determining the three-dimensional space distribution of the target block sand body. A three-dimensional model of the river channel sand body is established through a geostatistics modeling method; according to the method, geostatistics inversion and geostatistics modeling methods are comprehensively utilized, the three-dimensional river channel sand body model is established, and the method is of great significance to calculation of river channel sand body reserves and formulation of reasonable development and potential tapping measure adjustment; the method can be widely suitable for developing the sandstone oil field with a dense well pattern, the established river channel sand body model is high in precision, the requirements for oil field development and remaining oil potential tapping are met, and the oil field development effect is effectively promoted.

The present application relates to the field of oil and gas field exploration and development, especially to a method for seismic inversion of mechanical parameters of fracture-cave reservoir based on well logging stress constraint. Based on array sonic logging, the present application calculates the present-day stress size; based on pre-stack seismic data, the present application inverts different types of seismic attributes; through optimization of seismic attributes and threshold value, the present application realizes the sculpture of fracture-cave geometry; based on the sensitivity of fracture-cave seismic attributes, the present application establishes seismic identification standards of different types of fracture-cave; through rock mechanics parameter inversion cycle verification, the present application establishes rock mechanics parameter assignment models of different types of fracture-cave; considering stress constraint, the present application optimizes rock mechanics parameter assignment models of different types of fracture-cave, and finally realizes the three-dimensional rock mechanics parameter inversion of fracture-cave reservoir. The present application proposes a method for seismic inversion of mechanical parameters of fracture-cave reservoir based on well logging stress constraint, which has reference value for fracture-cave reservoir geomechanical modeling, stress field numerical simulation and "engineering sweet spot" evaluation and other aspects.

The invention relates to the technical field of seismic reservoir inversion prediction, in particular to a compact sandstone reservoir distribution prediction method based on two-dimensional earthquakes, terminal equipment and a storage medium, and aims to solve the problem of poor seismic waveform consistency at different survey line intersections of two-dimensional seismic inversion through survey line intersection trace pseudo-well constraint inversion, perform survey line consistency adjustment and improve the seismic waveform distribution prediction accuracy. And establishing a target function by using the inversion result at the intersection point of the measuring lines, solving the equation by adopting a simulated annealing method to obtain a parameter value, and finally adjusting the inversion result of each measuring line to obtain a final target equation. According to the method, the problem of poor seismic waveform consistency of a two-dimensional earthquake at the intersection point of different measuring lines can be improved, the inversion precision is improved, the distribution characteristics of the sandstone reservoir are accurately described, and technical support is provided for subsequent utilization and development of oil and gas resources.

This invention belongs to the field of reservoir prediction technology, specifically seismic inversion, and particularly relates to a method and apparatus for establishing an initial facies-controlled model in facies-controlled inversion. Based on sedimentary facies, this invention establishes a forward model and performs forward analysis according to the lithological combination characteristics and variation patterns of different facies zones. It then selects seismic attributes or attribute combinations that can distinguish different facies zones to establish seismic identification models for different sedimentary facies. These identification models are used to classify seismic facies, and the seismic facies control the establishment of the subsequent initial model, thereby making the initial model more reasonable and more sedimentary indicative.

The application provides a HTI medium elastic parameter and fracture weakness seismic inversion method and system, and comprises the following steps: establishing a seismic forward model based on a longitudinal wave reflection coefficient equation of the HTI medium and a convolution model of a seismic wavelet; according to the fact that a prior distribution of model parameters of the seismic forward model conforms to a Gaussian distribution, analyzing a prior probability distribution function and the seismic forward model by using a Bayesian theory, and then obtaining a posterior probability distribution function of the model parameters; and inversely solving the posterior probability distribution function of the model parameters to obtain the HTI medium elastic parameter and the fracture weakness. In combination with prior information of the model parameters, the uncertainty of an inversion result can be analyzed through the posterior probability distribution, and useful reference information is provided for subsequent seismic interpretation work.

The invention provides a method and related device for predicting a fracture-vug type carbonate rock surface karst reservoir, and the method comprises the following specific steps: obtaining the seismic section data of the surface karst of a to-be-detected region, removing the high amplitude information in the seismic section data through wavelet decomposition, and obtaining a seismic data volume reflecting the reservoir information; establishing an initial geologic model under phase control constraint; and performing seismic inversion of the initial geologic model by using the seismic data volume reflecting the reservoir information, and predicting to obtain a plane distribution range of the high-quality reservoir. According to the method, the prediction precision of the fracture-vug type carbonate rock surface karst reservoir is greatly improved, and later measures are effectively guided to tap potential remaining oil.