11 results about "Zoeppritz equations" patented technology

The invention provides a reservoir physical property parameter pre-stack seismic inversion method based on resistivity logging constraint. The method comprises the following steps: cutting observation data and depth; normalizing the seismic data, the resistivity data and the travel time to form network input; constructing a neural network framework, substituting the obtained porosity and saturation into an elastic rock physical model, and calculating to obtain an elastic parameter; the elastic parameters are substituted into a zoeppritz equation, and an underground reflection coefficient is obtained through calculation; generating a Ricker wavelet, and performing convolution calculation on the Ricker wavelet and the reflection coefficient to obtain predicted pre-stack seismic data; the porosity and the saturation are substituted into an Archie formula, and the predicted resistivity is obtained through calculation; constructing a target function; automatic differentiation is carried out, network parameters are updated, and then network output porosity and saturation are optimized. Reservoir parameter inversion driven by physical information is realized, and an intelligent scheme is provided for reservoir parameter prediction and evaluation in oil-gas exploration and development.

The present invention provides a prestack inversion method and device based on a gradient optimizer, relating to the technical field of prestack inversion, including: obtaining prestack angle gather data, horizon data, and elastic parameters of a target well log in a target work area; determining a solution space for the elastic parameters in the target work area based on the elastic parameters and horizon data; constructing a first objective function for inverting the elastic parameters in the target work area based on the prestack angle gather data of the target work area and the Zoeppritz equation; determining a first population within the solution space for solving the elastic parameters of a target seismic trace; iteratively updating the first population using a gradient optimizer to determine an optimal solution for the elastic parameters of the target seismic trace based on the angle gather data of the target seismic trace and the first objective function; and constructing an elastic parameter profile of the target work area based on the optimal elastic parameter solutions of multiple seismic traces. The application of the gradient optimizer enables the method of the present invention to simultaneously balance the convergence speed and inversion accuracy of prestack inversion.

The invention discloses a density attribute earthquake prediction method. The method comprises the following steps: forward modeling a longitudinal wave velocity, a transverse wave velocity and a density curve of saturated fluid rock; continuing forward modeling to generate a large number of longitudinal wave velocity, transverse wave velocity and density curves of different pseudo wells by using the bulk modulus and density parameters of the rock skeleton in the research area, and generating and synthesizing a pre-stack seismic gather under the guidance of a Zoeppritz equation; convolutional neural network training is carried out by using the longitudinal wave velocities, the transverse wave velocities and the density curves of the different pseudo wells and the synthetic pre-stack seismic gathers, and a trained convolutional neural network model is obtained; and applying the convolutional neural network model to an actual well and trace gather, correcting the trained convolutional neural network model, and performing density attribute prediction by using the corrected convolutional neural network model and applying the actual trace gather. Density attribute prediction is carried out through a deep learning method based on rock physical model sample expansion, and the problem that in the prior art, conventional elastic parameters cannot identify effective reservoirs under the medium-deep layer condition is solved.

The invention discloses a Young impedance and longitudinal and transverse wave velocity ratio nonlinear direct seismic inversion method based on an accurate YIRD-Zoeppritz equation. The Young impedance and longitudinal and transverse wave velocity ratio nonlinear direct seismic inversion method comprises the steps that the YIRD-Zoeppritz equation is obtained, and the Young impedance YI, the longitudinal and transverse wave velocity ratio PSR and the rock density are inversed through an ADA-MCMC-LpSC nonlinear inversion algorithm providing sparse prior information based on the Lp norm. Based on an accurate YIRD-Zoeppritz equation, the accurate prediction method for the Young impedance and the longitudinal and transverse wave velocity ratio under the ADA-MCMC-LpSC nonlinear optimization inversion algorithm is achieved, and the key geophysical problem that the accurate Zoeppritz equation cannot be directly used for predicting the Young impedance and the longitudinal and transverse wave velocity ratio parameters at the same time at present is solved.

The invention provides a reservoir velocity dispersion curve inversion method and device, and the method comprises the steps: calculating a single-interface reflection coefficient through employing a Zoeppritz equation, and carrying out the recursion of a layered medium reflection coefficient through combining with a reflectivity method; establishing an AVAF forward modeling equation based on the frequency domain convolution model; establishing an inversion objective function by using the difference between the frequency spectrum real part and the frequency spectrum imaginary part of the actual seismic record and the synthetic seismic record, and determining the upper limit and the lower limit of a to-be-inverted parameter; the method comprises the following steps: constructing a Metropolis-hot bath inversion framework by fusing an acceptance-rejection criterion of a Metropolis algorithm and a one-by-one parameter optimization thought of a hot bath algorithm, and carrying out multiple times of global optimization inversion based on an improved method to obtain an optimal reservoir thickness and speed frequency dispersion curve. According to the method provided by the invention, the reservoir thickness and the velocity values under different frequencies in the seismic frequency band can be inversely estimated, and a powerful tool is provided for reservoir prediction and fluid identification.

The present invention discloses a pre-stack AVA elastic parameter inversion method and system based on knowledge transfer. A forward model is constructed based on the exact Zoeppritz equation to implement multiple groups of differential evolution algorithms with mutation. By introducing the optimal solution information of the elastic parameters of the bypass channel through the idea of knowledge transfer, while accelerating the iteration convergence of the current channel, the lateral continuity of the inverted elastic parameter profile is ensured. By introducing the post-stack P-wave impedance information during the selection process of multiple groups of differential evolution algorithms with mutation, the relationship between parameters is constrained, and the correlation of the three inverted parameters is improved; thereby ultimately improving the accuracy of pre-stack AVA elastic parameter inversion and ensuring lateral continuity. The basis of the present invention is for the known pre-stack AVA forward angle gather in the model, and the single-channel inversion of elastic parameters is performed using the MMDE method based on the given initial model. Then, to ensure lateral continuity, knowledge transfer is used to map the results of the bypass channel to the inversion process of the current channel, and finally, a laterally continuous elastic parameter inversion result is obtained.

The invention discloses a microfracture porosity seismic rock physics linear inversion method, and belongs to the field of oil and gas reservoir exploration. The method comprises the following steps: firstly, constructing an earthquake rock physical forward model by combining a dual-dual pore medium DDP-BR equation as a core and a Zoeppritz equation, establishing a quantitative mapping relation between physical parameters and earthquake data, calculating a Frechet derivative of a forward operator to the physical parameters, and realizing earthquake rock physical linear inversion based on DDP-BR by adopting an LBFGS-MT algorithm. The method comprises the following steps of: firstly, inverting hard hole local porosity, micro-crack local porosity, inclusion content and water saturation by adopting seismic data; and secondly, indirectly estimating the porosity of the hard hole, the porosity of the micro-crack and the total porosity, and avoiding the problem of inversion instability caused by strong coupling among parameters. A synthetic data test verifies that the method has good stability and inversion precision, and microfracture development information of a complex reservoir can be effectively estimated.

The invention discloses a shale oil reservoir geology and engineering double-dessert quantitative evaluation method and system. The method comprises the following steps: acquiring pre-stack earthquake and logging data; based on a Zeoppritz equation, utilizing a pre-stack seismic inversion technology to obtain reservoir elastic parameters; calculating longitudinal and transverse wave impedance according to the reservoir elastic parameters; calculating a brittleness index as an engineering dessert parameter according to the reservoir elastic parameter and the longitudinal and transverse wave impedance; calculating the bulk modulus and shear modulus of the reservoir according to the elastic parameters of the reservoir; obtaining TOC content, porosity and free hydrocarbon as geological dessert parameters by combining logging data, reservoir elastic parameters, bulk modulus and shear modulus; calculating formation pressure; and performing comprehensive quantitative evaluation based on the engineering dessert parameters, the geological dessert parameters and the formation pressure. According to the method, the overlapping area of the geological and engineering double sweet spots is effectively identified, well position deployment and fracturing design are optimized, the balanced fracturing effect is ensured, the oil and gas recovery efficiency is improved, and the exploitation efficiency is improved.

This specification relates to the field of seismic data processing and interpretation technology, and provides a method, apparatus, device, storage medium, and product for predicting gas saturation. The method includes: acquiring pre-stack angle gather data of a target reservoir; performing pre-stack inversion on the pre-stack angle gather data using the Zoeppritz equation to obtain the elastic parameters of the target reservoir; and inputting the elastic parameters of the target reservoir into a pre-trained gas saturation prediction model to obtain the gas saturation of the target reservoir. The gas saturation prediction model is trained based on a KAN model, which uses a learnable activation function to capture the nonlinear relationship between the elastic parameters and the gas saturation. This specification's embodiments can improve the accuracy of gas saturation prediction.

The application provides a reservoir physical parameter prestack seismic inversion method constrained by resistivity logging, which cuts observation data and depth; normalizes seismic data, resistivity data and travel time to form network input; constructs a neural network framework, substitutes obtained porosity and saturation into an elastic rock physics model to calculate elastic parameters; substitutes the elastic parameters into a Zoeppritz equation to calculate underground reflection coefficient; generates a Ricker wavelet, and performs convolution calculation on the reflection coefficient to obtain predicted prestack seismic data; substitutes the porosity and saturation into an Archie formula to calculate predicted resistivity; constructs an objective function; performs automatic differentiation to update network parameters, and further optimizes network output porosity and saturation. The application realizes reservoir parameter inversion driven by physical information, and provides an intelligent scheme for reservoir parameter prediction and evaluation in oil and gas exploration and development.

The invention relates to the technical field of geophysics, seismic exploration, oil-gas exploration and tunnel and underground engineering, and discloses a pre-stack generalized progressive-order Taylor expansion inversion method, which comprises the following steps of: establishing an initial model of longitudinal wave velocity, transverse wave velocity and density by using logging information, and constructing a prior model through a damping least square method; constructing a first-order Taylor expansion form and a second-order Taylor expansion form of the synthetic seismic record based on an accurate Zoeppritz equation, and introducing the first-order Taylor expansion form and the second-order Taylor expansion form as double targets into a multi-target arithmetic optimization algorithm for joint solving; the balance of global search and local approximation is realized through an arithmetic operator, and a non-dominated solution set is maintained by means of an elite archive, so that an inversion result of a continuous progressive order from first-order approximation to second-order approximation is obtained; and finally, determining an elastic parameter inversion result according to a minimum oscillation and transverse consistency criterion. According to the method, the stability and prediction reliability of pre-stack AVA inversion can be improved.