169 results about "Seismic inversion" patented technology

The invention relates to the technical field of unconventional oil-gas exploration, and discloses a well-seismic integrated horizontal well geosteering risk evaluation method, which comprises the following steps: S1, acquiring three-dimensional seismic data, measurement while drilling data and geological logging data through a well-seismic integrated data acquisition platform; s2, constructing a horizontal well geosteering dynamic model based on multi-source heterogeneous data, and integrating a seismic inversion result and real-time drilling parameters; by establishing a well-seismic data dynamic fusion mechanism and a multi-source risk quantification model, the problem of space-time dislocation of seismic inversion and measurement-while-drilling data is solved, the recognition precision of a fault boundary, a lithologic interface and a pressure abnormal zone is ensured to be matched with drilling position changes in real time, risk misjudgment caused by model updating lag in a traditional method is eliminated, and the accuracy of the method is improved. The accuracy and timeliness of geosteering decision making of the complex structure area are improved; by monitoring well track deviation and geological model prediction deviation in real time, the risks of well wall instability and target deviation are avoided, and the operation safety of the horizontal well in the full life cycle is guaranteed.

The present disclosure discloses a seismic inversion method based on joint constraint of a physical model and priori information. The method includes: extracting seismic wavelets based on seismic data, and determining an amplitude scaling factor of the wavelets; counting priori information of impedance parameters; establishing an initial impedance parameter model by using seismic structural interpretation information and logging data; obtaining a simplified approximate equation based on an interface weak elasticity difference hypothesis, forward modeling a seismic gather by using the simplified equation, and calculating an inversion residual; rewriting an objective function into a function related to the impedance parameters by using a generalized linear inversion idea, solving the impedance parameters by using an iterative reweighted least squares algorithm, and updating the impedance parameters; and repeating the above steps until the inversion residual reaches the requirements or reaches the maximum number of iterations, and outputting a final processing result.

The invention discloses a stochastic finite fault source model establishment method, which comprises the following steps of S1, determining a geometrical shape of a seismic fault based on regional geological data and seismic monitoring data, and estimating a non-planar fault parameter and a fault plane average sliding amount in combination with historical data and an empirical formula; s2, estimating distribution and sliding strength of concave-convex bodies and obstacles on the fault surface according to historical seismic inversion data, and obtaining the sliding amount of each sub-fault by randomly disturbing the average sliding amount; s3, the discrete fault plane serves as a sub-fault unit, and after an initial fracture point and the average fracture speed are determined, random fracture initial time conforming to normal or power law distribution is generated based on a random number method; s4, calculating the seismic oscillation time history of each sub-fault by adopting a random point source method, superposing contributions, introducing the influence of a shallow velocity structure V30, and generating a three-dimensional seismic oscillation field; and S5, comparing actual strong earthquake record adjustment parameters, and outputting a multi-risk-level earthquake motion parameter evaluation result after iterative optimization. According to the method, by fusing randomness and empirical data, the simulation precision of the seismic source model on the uncertainty of the seismic process is improved, the influence of the potential maximum-magnitude earthquake possibly occurring in the active fault zone in the future can be effectively estimated, the seismic oscillation simulation precision and the calculation efficiency can be improved, and the method is suitable for engineering structure aseismic design and seismic disaster evaluation.

The invention is suitable for the technical field of geophysical exploration, and provides a rock physics-driven time-frequency domain longitudinal wave velocity dispersion attribute prediction method, which comprises the following steps of: establishing a rock physics model; under the driving of the rock physical model, inverting the porosity of the microfractures in the well, calculating the skeleton elastic modulus of the rock containing the fractures, and predicting a frequency-dependent longitudinal wave velocity logging curve; constructing a time-frequency domain longitudinal wave velocity seismic inversion initial model; constructing a TF-AVO theoretical formula, developing a time-frequency domain longitudinal wave velocity seismic inversion method based on an L-BFGS algorithm, and performing longitudinal wave velocity inversion by taking the initial model as prior information; analyzing the relative change of the frequency-varying longitudinal wave speed under different frequencies, and quantitatively predicting the frequency dispersion attribute of the longitudinal wave speed; and on the basis of logging data, calibrating a longitudinal wave velocity dispersion attribute prediction result, and carrying out reservoir oil and gas detection. The method has higher precision and resolution, the stability and reliability of longitudinal wave velocity dispersion attribute prediction are remarkably improved, and the dependence on strong amplitude response is overcome.

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 invention discloses a geosteering method for a low-permeability sandstone reservoir gas horizontal well, and belongs to the technical field of oil and gas development. The invention discloses a geosteering method for a low-permeability sandstone reservoir gas horizontal well. The geosteering method comprises the following steps: designing an initial trajectory based on seismic inversion and adjacent well data; gR, RT, DT and CNL data while drilling are collected in real time; space-time calibration is implemented through a deep matching algorithm, and quality control is performed by using multi-parameter cross validation; calculating a reservoir matching degree index SMI based on a geological rule and a machine learning model, and generating an adjustment suggestion when the SMI is lower than a threshold value; verifying feasibility by combining drilling engineering constraints, and dynamically optimizing the trajectory by taking a high-quality reservoir contact rate greater than or equal to 90% as a target; and after drilling is completed, the reservoir drilling encounter rate is quantitatively analyzed. According to the method, multi-source data accurate fusion and intelligent trajectory adjustment are achieved, the problem of low-permeability sandstone reservoir trajectory control is solved, and the drilling efficiency and the recovery efficiency are improved.

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 present application relates to a kind of low wave number initial model establishment method, device and medium of depth domain seismic inversion, method includes: extracting the well logging data of target layer section, the logging attribute of the low wave number initial model to be built is calculated;According to well seismic calibration result, extract well seismic data of well logging section, and according to logging depth sampling interval is up-sampled, obtain well seismic data on well logging depth section;The amplitude attribute of well seismic data section is calculated;With the amplitude attribute of well seismic data section as independent variable, the logging attribute of the low wave number initial model to be built is function value, linear fitting is carried out;From the fitting result, the amplitude attribute of optimal linear correlation is selected, and the optimal slope parameter and optimal intercept parameter are obtained;The minimum amplitude attribute of corresponding depth domain seismic data body is calculated;Depth domain seismic data body amplitude attribute is converted into the attribute of low wave number initial model to be built;Low wave number initial model attribute to be built is carried out Gaussian filtering, and the final low wave number initial model is obtained.

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 discloses a seismic inversion evaluation method and device, and the method comprises the steps: obtaining the seismic data and drilling data of a target layer, and calculating the signal-to-noise ratio and dominant frequency of the seismic data of the target layer; determining a lithofacies type of the target layer and a corresponding seismic waveform feature according to the seismic data and the drilling data; calculating a correlation coefficient between each lithofacies type and the corresponding seismic waveform feature according to the lithofacies type and the corresponding seismic waveform feature; generating a seismic waveform classification plane graph according to the lithofacies type and the corresponding seismic waveform features; counting the drilling density of each lithofacies type according to the seismic waveform classification plane graph; comprehensive evaluation indexes are calculated; the comprehensive evaluation index is used for evaluating the seismic inversion credibility. According to the invention, effective pre-evaluation of seismic inversion credibility can be realized, and the risk of oil-gas exploration and development is reduced.

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 present application relates to the field of geophysical inversion and oil and gas reservoir prediction, and particularly relates to a method for predicting fractures of lacustrine carbonate rock based on fractional order total variation. The method comprises the following steps: obtaining an initial model of a to-be-inverted parameter by preprocessing seismic data; constructing a forward model of prestack elastic parameters and an objective function based on fractional order total variation; updating the inversion of the log matrix L by combining the alternating direction multiplier method, the gradient being zero and the extreme value, and eigenvalue decomposition; obtaining Young's modulus, Poisson's ratio and density according to the relationship between Young's modulus, Poisson's ratio and the elastic three-parameter log matrix L i+1 ; and obtaining the fracture prediction result by slicing the inversion result of Young's modulus and Poisson's ratio according to the horizon. The present application uses fractional order total variation to establish a sparse regularization term and construct an objective function of prestack elastic three-parameter seismic inversion, thereby improving the accuracy and stability of the inversion result and the accuracy of fracture prediction.

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.

A system and method for generating synthetic high-frequency seismic data for subsurface imaging comprises injecting a first seismic shot having a low frequency into a surface region and receiving reflected waves at a plurality of seismic receivers. Seismic traces are recorded and processed to generate a low-frequency seismic shot gather. A second seismic shot having a high frequency is injected, and a sparse number of high-frequency traces are recorded. A computing device resamples the low-frequency traces to match the sparse high-frequency dataset and applies a one-dimensional (1D) local matching filter to transform the low-frequency traces into simulated high-frequency traces. The transformed dataset is used to generate a high-resolution subsurface image of geological interfaces. The system enables computationally efficient high-frequency seismic data synthesis, optimizing seismic inversion accuracy while reducing acquisition costs and computational overhead.

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 an elastic parameter high-frequency component recovery method based on a conditional generative adversarial network, and comprises the following steps: acquiring large-scale elastic parameter information of a research area; inputting the large-scale elastic parameter information of the research area into a pre-constructed conditional generative adversarial network; the conditional generative adversarial network comprises a discriminator D and a generator G, and the generator G outputs corresponding elastic parameter information containing high-frequency components. After the conditional generative adversarial network is successfully trained, high-precision elastic parameter information containing large-scale elastic parameter information and small-scale elastic parameter information conforming to the geological law of the research area can be output by the generator only by providing the large-scale elastic parameter information of the research area, so that the recovery of the high-precision elastic parameter is realized. Higher-precision seismic data is provided, and seismic inversion is better carried out.

The application provides a seismic inversion method and system based on dynamic change of Lagrange operators, and the method comprises the following steps: obtaining a column vector composed of 2n+1 Lagrange operators according to a preset prior velocity model and an initial velocity model, wherein n is a positive integer; substituting the column vector into a preset inversion iteration formula to obtain a corresponding number of inversion velocity models, and obtaining a target function according to the inversion velocity models; determining an inversion velocity model with the minimum function value of the target function as an iteration model, and repeatedly determining the iteration model until the minimum value of the target function reaches a preset value of the target function. The application can facilitate rapid reduction of the target function by using dynamic Lagrange operators in the inversion process, and ensure the speed and accuracy of the inversion result.

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 a dual-pore structure factor permeability earthquake prediction method and system, a medium and equipment. The method comprises the following steps: acquiring logging data, core experiment data and earthquake data of a target area; performing inversion based on the rock physical model to obtain pore structure parameters and a dynamic soft / hard pore aspect ratio; performing model construction according to the porosity, the pore structure parameters and the dynamic soft / hard pore aspect ratio to obtain a multivariate nonlinear regression model; training and verifying the multivariate nonlinear regression model and the neural network by using a training set in the logging data to obtain a permeability regression model; and carrying out inversion based on the seismic data to obtain inversion data, and carrying out prediction on the inversion data and the permeability regression model to obtain a permeability body. According to the method, three-dimensional quantitative prediction of the permeability of the low porosity and permeability reservoir is realized on the basis of the double-pore structure factors in combination with the pre-stack seismic inversion and neural network technologies, the prediction precision can be remarkably improved, and reliable basis and technical support are provided for efficient development of oil and gas reservoirs.

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.

The invention discloses a sand-rich oil and gas reservoir exploration method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining seismic data volumes of a thick sand layer section and top and bottom mudstone sections of the thick sand layer section, and converting the seismic data volumes into seismic inversion data volumes; based on the seismic inversion data volume, determining a seismic attribute value domain for sand body identification; generating a mudstone section data volume based on the seismic inversion data volume and the seismic attribute value domain; determining an updip sand body with an updip tip based on the mudstone section data body; and determining a trap range of the updip sand bodies based on the updip sand bodies, and determining the resource quantity of each updip sand body based on the trap range. The technical problem that lithologic sand-rich oil and gas reservoir exploration under the sand-rich background cannot be realized in the prior art is solved.

The application provides a formation pressure prediction method and device, electronic equipment and storage medium. The method comprises the following steps: obtaining well logging data of a target formation; performing seismic inversion according to the well logging data of the target formation to obtain an inversion result; constructing a first pressure prediction model of the target formation corresponding to an undercompaction cause, a second pressure prediction model of the target formation corresponding to a hydrocarbon generation and pressure increase cause, and a third pressure prediction model of the target formation corresponding to a tectonic extrusion cause according to the inversion result; and fusing the first pressure prediction model, the second pressure prediction model and the third pressure prediction model by using a fuzzy logic algorithm to obtain a pressure prediction result of the target formation. The method realizes complex cause formation pressure prediction based on multi-information joint, and improves the drilling pre-prediction accuracy of formation pressure under complex geological conditions.