38 results about "Bayesian inversion" patented technology

The invention discloses a lithofacies constrained reservoir physical property parameter inversion method and device. The lithofacies constrained reservoir physical property parameter inversion method comprises the steps of establishing a target inversion function based on lithofacies constraint according to a Bayes inversion frame and lithofacies information; introducing the lithofacies information to establish a statistical rock physical lithofacies model; utilizing a stochastic simulation technology to produce an elastic parameter and reservoir physical property parameter training sample set including the lithofacies information based on the statistical rock physical lithofacies model; inputting pre-stack seismic inversion result data and lithofacies data, and utilizing the training sample set to solve the target inversion function based on the lithofacies constraint. The lithofacies constrained reservoir physical property parameter inversion method and device uses the lithofacies information as inversion constraint information, enables an inversion principle to be more rigorous and enables the inversion process to be more reasonable by adding the lithofacies constraint and can greatly decrease multiple solutions of an inversion result. The derived target inversion function includes the lithofacies information, the solving process is more targeted, the result is more reliable, and the favorable position of a reservoir can be accurately recognized.

A reservoir fluid identification method based on pore fluid parameter frequency dependence inversion is disclosed. The method comprises the following steps of carrying out amplitude preservation, denoising and offset processing on pre-stack seismic data; carrying out different-angle stack to acquire different-angle-stack seismic data of at least three angles; establishing a pore fluid parameter and a shear modulus model, extracting an angle wavelet and carrying out wavelet multi-scale frequency division processing on the different angle stack seismic data; carrying out frequency dependence attribute inversion of the pore fluid parameter; and according to a pore fluid parameter frequency dependence attribute evolution value, determining a reservoir fluid development situation. By using the method, received seismic information is used and a deceptive problem in conventional fluid identification can be well solved; a sensibility of a dependence inversion attribute of a pore-fluid sensitive parameter to a reservoir fluid is high and identification precision to the fluid is high; an Bayesian inversion theory is used to carry out inversion so that stability and reliability are high.

A method and apparatus for estimating a porosity and a saturation in a subsurface reservoir. The method includes determining a plurality of mathematical relationships relating a plurality of fundamental physical parameters that govern elastic wave propagation in the subsurface reservoir to the porosity and the saturation in the subsurface reservoir, forward modeling a plurality of seismic attributes using the mathematical relationships to derive a plurality of conditional probability density functions for the seismic attributes, applying a Bayesian inversion to the conditional probability density functions for the seismic attributes to derive a joint probability density function for the porosity and the saturation in the subsurface reservoir, and integrating the joint probability density function for the porosity and the saturation to derive a probability density function for the porosity and a probability density function for the saturation.

The invention discloses a facies-controlled porosity inversion method based on Bayesian classification and belongs to the technical field of petroleum geology exploration. The facies-controlled porosity inversion method includes the following steps: 1. perform cross analysis on a well logging curve of rock porosity and the well logging curve of a plurality of rock elastic parameters which is acquired through experiment tests to acquire the rock elastic parameter data sensitive to the rock porosity; 2. acquiring the screened porosity and the screened elastic parameter data; 3. establishing a borehole-seism initial model and also acquiring the prior probability distribution of the screened porosity; 4. acquiring resample porosity and also acquiring fitting screening elastic parameter data and fitting resampling elastic parameter data; 5. acquiring resampling errors; 6. acquiring reconstructed elastic parameter data; 7. acquiring the prior sample series and conducting Bayesian classification to acquire the prior probability distribution and posterior probability distribution; and 8. conducting Bayesian inversion to transform a known elastic parameter data volume to a porosity data volume.

A method for evaluating measured electromagnetic (EM) data relating to a subsurface region, comprising the steps of: (a) specifying at least one model of the region in terms of fundamental parameters with uncertainty, using a fundamental inversion grid; (b) receiving the measured EM data and an estimated error; (c) translating the fundamental parameters of the model to meta parameters of the region and to a computational grid suitable for forward modelling and comparison to the measured EM data, using relationships with uncertainty; and (d) carrying out a Bayesian inversion using the measured data and estimated error to produce an output comprising fundamental parameters of the region on the fundamental inversion grid with uncertainty. The method allows physical measurements with error to be translated into fundamental parameters which can be used to assess business risk and uncertainty for making decisions, and also provides for parameters and models which can typically be directly estimated by a geoscientist on a coarse spatial grid to be used as an input, and validated using the measured data with error.

The invention relates to a medical X-ray device 5 arrangement for producing three-dimensional information of an object 4 in a medical X-ray imaging medical X-ray device arrangement comprising an X-ray source 2 for X-radiating the object from different directions and a detector 6 for detecting the X-radiation to form projection data of the object 4. The medical X-ray device 5 arrangement comprises:

• • means 15 for modelling the object 4 mathematically independently of X-ray imaging
  • and means 15 for utilizing said projection data and said mathematical modelling of the object in Bayesian inversion based on Bayes' formula $p\text{(}xm)=\frac{p_{\mathrm{pr}}\left(x\right)p(mx)}{p\left(m\right)}$

to produce three-dimensional information of the object, the prior distribution p_pr(x) representing mathematical modelling of the object, the object image vector x, which comprise values of the X-ray attenuation coefficient inside the object, m representing projection data, the likelihood distribution p(m|x) representing the X-radiation attenuation model between the object image vector x and projection data m, p(m) being a normalization constant and the posteriori distribution p(x|m) representing the three-dimensional information of the object 4.

A medical X-ray device 5 arrangement for producing three-dimensional information of an object 4 in a medical X-ray imaging comprises an X-ray source 2 for X-radiating the object from at least two different directions; a detector 6 for detecting the X-radiation to form projection data of the object 4; a computational device 15 for modelling the object 4 mathematically utilizing the projection data to solve the imaging geometry and/or the motion of the object, where the solving concerns either some or all parts of the imaging geometry and/or the motion of the object. The computational device 15 utilizes said projection data and said mathematical modelling of the object in Bayesian inversion based on Bayes' formula $p\left(x,\theta ❘m\right)=\frac{p_{\mathrm{pr}}\left(\theta \right)p_{\mathrm{pr}}\left(x\right)p\left(m❘x,\theta \right)}{p\left(m\right)}$

to produce three-dimensional information of the object.

The invention discloses a broad earthquake wave impedance low-frequency information prediction method, comprising the steps of utilizing broadband overlapped earthquake information and earthquake wavelet to establish complex frequency domain earthquake impedance calculation; utilizing Bayesian inversion theory to calculate initial target function of earthquake wave impedance according to positive calculation of complex frequency domain earthquake impedance; adding initial model constraint to initial target function of earthquake wave impedance to get low-frequency prediction target function and work out the low-frequency prediction target function to get earthquake wave impedance low-frequency information. The method is only utilizes broadband earthquake materials to realize low-frequency prediction of earthquake wave impedance, that is to say, shaft information constraint is not required to measure, and low-frequency information prediction result can still be obtained if complicated geological conditions such as lenticle and strong change of physical property exist. The invention also discloses a broad earthquake wave impedance low-frequency information prediction system, having the above beneficial effect.

The invention provides a landslide uncertainty model dynamic construction method based on multi-source heterogeneous data fusion. The landslide uncertainty model dynamic construction method comprisesthe steps of obtaining spatial variation information of landslide rock-soil mass mechanical parameters through combined application of geological survey, data collection, indoor mechanical tests, CT scanning and numerical sample random reconstruction technologies; constructing a landslide initial uncertainty model on the basis of spatial law statistics of mechanical parameters of the rock-soil body; carrying out landslide numerical simulation under corresponding working conditions in a non-invasive random finite element mode; constructing an intelligent response surface model of landslide output information and random variables at different time points, and updating an uncertainty model in real time by utilizing landslide direct information and monitoring indirect data based on the intelligent response surface model and an adaptive condition sampling reliability Bayesian inversion method, so that a multisource heterogeneous data fused landslide uncertainty model is established. The method has the advantages that a fine landslide geologic model is established, and an effective means is provided for subsequent landslide mechanism analysis and stability evaluation.

The invention belongs to the technical field of geoacoustic parameter inversion, and particularly relates to a shallow sea multilayer seabed geoacoustic parameter inversion method based on a nonlinearBayesian theory, which comprises the following steps of: inverting seabed geoacoustic parameters according to a nonlinear Bayesian inversion method to accurately judge an actual seabed structure frommeasured data; adopting the correction simulated annealing algorithm, so that the accuracy and the discrimination degree of an inversion result can be improved; compared with two types of sound velocity attenuation and density, determining that the uncertainty of the longitudinal wave sound velocity and the transverse wave sound velocity is smaller; and compared with a semi-infinite substrate, determining that the uncertainty of each parameter in the deposition layer is smaller.

A method for evaluating measured electromagnetic (EM) data relating to a subsurface region, comprising the steps of: (a) specifying alternative models of the region in terms of input parameters with uncertainty; (b) receiving the measured EM data and an estimated error; and (c) carrying out a Bayesian inversion on the measured data using each of the alternative models to attribute a probability to each model on the basis of the measured data and estimated error. The method allows physical measurements with error to be translated into fundamental parameters which can be used to assess business risk and uncertainty for making decisions, and also provides for parameters and models which can typically be directly estimated by a geoscientist on a coarse spatial grid to be used as an input, and validated using the measured data with error.

The invention provides a method and a system for inverting underground mining parameters based on InSAR and Okada models. The method comprises the following steps: acquiring surface deformation of a mining area by using an InSAR technology; constructing a function relationship between the surface deformation quantity and the model parameters; and according to the surface deformation quantity and the function relationship, obtaining actual mining parameters by adopting a Bayesian inversion algorithm. According to the method, the earth surface deformation caused by underground mining is obtained through a time sequence synthetic aperture radar interferometry technology, and denser original data is provided for inversion. Besides, for a mining area working face, under the condition that mining parameters are unknown or few mining parameters are known, an Okada model of underground mining surface deformation is established, reliable actual mining parameters are obtained through a Bayesian inversion algorithm by means of surface deformation observation data collected by the InSAR, and scientific formulation of an underground mining plan, monitoring of border crossing mining and the like are facilitated.

The invention provides a fractured shale gas reservoir performance parameter prediction method and system, and belongs to the technical field of shale gas reservoir exploration, and the method comprises the steps of obtaining a Fourier coefficient data volume based on azimuth gather data in combination with Fourier series decomposition; based on the logging data, obtaining an isotropic parameter low-frequency model and an anisotropic parameter low-frequency model; obtaining effective stress sensitive parameters based on Bayesian inversion by using the Fourier coefficient data volume, the isotropic parameter low-frequency model and the angle seismic wavelet; and utilizing the Fourier coefficient data volume, the anisotropy parameter low-frequency model and the angle seismic wavelet to obtain a fracture weakness parameter based on Bayesian inversion. According to the invention, reasonable effective stress sensitive parameters and fracture weakness prediction results can be generated, and the abnormal pressure and fracture development characteristics of the fractured shale gas reservoir can be identified.

The invention provides a shale reservoir fracture and brittleness prediction method based on Bayesian inversion. The method comprises the steps that azimuth part angle superposition seismic data, azimuth seismic wavelets and a model parameter low-frequency model are acquired; a prediction result is obtained according to the obtained azimuth part angle superposition seismic data, azimuth seismic wavelets, a model parameter low-frequency model and a preset shale reservoir fracture and brittleness prediction model; an establishment process of the shale reservoir fracture and brittleness prediction model comprises the following steps: deducing an HTI medium longitudinal wave azimuth reflection coefficient approximate equation containing a new brittleness indication factor and fracture density; and a shale reservoir fracture and brittleness prediction model can be established according to the relationship and a Bayesian AVAZ inversion method. According to the method, accumulative errors in the parameter indirect conversion process are effectively avoided, and the shale reservoir fracture and brittleness prediction precision is improved.

The invention discloses a VTI equivalent medium crack weakness parameter seismic inversion method and system. The method comprises the following steps: acquiring seismic data of a to-be-researched work area; acquiring an actual logging curve of the to-be-researched work area, wherein the actual logging curve is a logging curve of the elastic characteristic parameters and the fracture weakness; smoothing the actual logging curve of the to-be-researched work area to obtain an initial model of elastic parameter and crack weakness parameter inversion; according to the initial model of the elastic parameter and crack weakness parameter inversion, obtaining a to-be-estimated new attribute parameter by using a scattering theory and a weak anisotropy approximate hypothesis condition; and according to seismic data of a to-be-researched work area, based on a Bayesian inversion theory, performing seismic inversion on the to-be-estimated new attribute parameter to obtain an elastic modulus and a fracture parameter of the shale gas reservoir. According to the method, the reservoir elasticity modulus and the fracture weakness parameter can be reasonably and reliably obtained from the pre-stack seismic data, so that fine description and depiction of shale gas reservoir features are realized.

The invention discloses a seismic data inversion non-stretching dynamic correction method and device, electronic equipment and a medium. The method comprises the following steps: positioning the position of a reflection coefficient by reference channel Bayesian inversion; determining an objective function of sparse reflection coefficient inversion by taking the position of the reflection coefficient as a constraint; solving by a least square method aiming at an objective function of sparse reflection coefficient inversion, and calculating a reflection coefficient after dynamic correction; and calculating seismic data after dynamic correction through the reflection coefficient after dynamic correction. According to the method, a dynamic correction result without stretching distortion is obtained, and the purposes of improving the data quality of the angle gather and facilitating subsequent processing and interpretation work are achieved.

The invention provides a joint Bayesian inversion method and system for lithologic and physical parameters of a reservoir. The method comprises the following steps: establishing a rock physical model and a statistical prior model according to core or logging data; constructing an optimization problem of joint inversion based on the Bayesian theory; and solving the problem by adopting a continuous-discrete variable genetic algorithm. According to the method, thoughts of lithology classification and physical property parameter estimation are combined, a new method of joint Bayesian inversion of reservoir lithology and physical property parameters is developed, quantitative prediction of underground reservoir properties is achieved, and then reliable support is provided for fine oil and gas exploration and development.