Optimizing seismic to depth conversion to enhance subsurface operations including measuring seismic data in a subsurface formation, dividing the subsurface formation into a training area and a study area, dividing the seismic data into training seismic data and study seismic data, wherein the training seismic data corresponds to the training area, and wherein the study seismic data corresponds to the study area, calculating target depth data corresponding to the training area, training a machine learning model using training inputs and training targets, wherein the training inputs comprise the training seismic data, and wherein the training targets comprise the target depth data, computing, by the machine learning model, output depth data corresponding to the study area based at least in part on the study seismic data; and modifying one or more subsurface operations corresponding to the study area based at least in part on the output depth data.

A computer system receives a post-stack time-domain image having a first spectrum and representing one or more subsurface structures. The computer system reconstructs an increased-frequency version of the post-stack time-domain image using L0-constrained inversion and a least-squares mismatch ratio. The increased-frequency version of the post-stack time-domain image includes structural artifacts. The computer system removes the structural artifacts from the increased-frequency version of the post-stack time-domain image using singular value decomposition. The computer system combines the increased-frequency version of the post-stack time-domain image with the post-stack time-domain image using a weighting function. The computer system generates a combined version of the increased-frequency version of the post-stack time-domain image and the post-stack time-domain image. The combined version represents the one or more subsurface structures and has a second spectrum broader than the first spectrum.

The invention discloses a dolomite reservoir prediction method and system based on well and seismic combination, and storage medium. The method steps include: obtaining the dolomite index characteristic curve through well log sensitivity analysis, and distinguishing the dolomite and limestone according to the difference in their response range; after the artificial intelligence deep learning is performed on the dolomite index characteristic curve of the drilling area, the dolomite index characteristic curve of the virtual drilling area is obtained; according to the dolomite index characteristic curve of the drilling area and the virtual drilling area, the post-stack seismic data is used for inversion to obtain the distribution and development status of the dolomite reservoir in the test area. The invention effectively distinguishes the dolomite and limestone through the dolomite index characteristic curve, and accurately predicts the distribution and development status of the dolomite reservoir in the test area with less wells.

The invention belongs to the field of environmental monitoring, and in particular relates to a method for optimally selecting a carbon storage site based on multi-frequency band seismic data. The method comprises the steps of: performing seismic wavelet spread spectrum simulation based on three-dimensional post-stack seismic data to obtain spread spectrum simulated wavelets; building an isochronous stratigraphic framework model of a target horizon, and calculating the geometric structure and spatial distribution of a fault-karst; then performing waveform-indicated inversion to obtain a wave impedance inversion data volume, and obtaining a stable stratum wave impedance data volume through a virtual well cross-well wave impedance interpolation; calculating the difference between the stable stratum wave impedance data volume and the wave impedance inversion data volume to obtain an abnormal wave impedance data volume, then obtaining a fault-karst reservoir bed interpretation model, and determining the position of a carbon storage box.

The invention discloses a dolomite reservoir prediction method and system based on well and seismic combination, and storage medium. The method steps include: obtaining the dolomite index characteristic curve through well log sensitivity analysis, and distinguishing the dolomite and limestone according to the difference in their response range; after the artificial intelligence deep learning is performed on the dolomite index characteristic curve of the drilling area, the dolomite index characteristic curve of the virtual drilling area is obtained; according to the dolomite index characteristic curve of the drilling area and the virtual drilling area, the post-stack seismic data is used for inversion to obtain the distribution and development status of the dolomite reservoir in the test area. The invention effectively distinguishes the dolomite and limestone through the dolomite index characteristic curve, and accurately predicts the distribution and development status of the dolomite reservoir in the test area with less wells.

A method includes receiving seismic data for a geologic region of the Earth; building a velocity model of the geologic region of the Earth; selecting at least one mode of multiple and corresponding travel time data from a data storage where the travel time data correspond to at least one complex ray signature in the geologic region of the Earth and are based at least in part on the velocity model; performing migration on the seismic data using at least the selected travel time data to generate processed seismic data; and rendering an image of the geologic region of the Earth to a display where the image includes at least a multiple image.

A method and apparatus for seismic analysis include obtaining an initial geophysical model and seismic data for a subsurface region; producing a subsurface image of the subsurface region with the seismic data and the geophysical model; generating a map of one or more geologic features of the subsurface region by automatically interpreting the subsurface image; and iteratively updating the geophysical model, subsurface image, and map of geologic features by: building an updated geophysical model based on the geophysical model of a prior iteration constrained by one or more geologic features from the prior iteration; imaging the seismic data with the updated geophysical model to produce an updated subsurface image; and automatically interpreting the updated subsurface image to generate an updated map of geologic features. The method and apparatus may also include post-stack migration, pre-stack time migration, pre-stack depth migration, reverse-time migration, gradient-based tomography, and/or gradient-based inversion methods.

The present invention is related to a specific de-migration method based on ray tracing algorithms characterized in that the interpolation procedure involved in the computation of the travel time required by the de-migration is being modified. The interpolation according to the invention obtains an accurate travel time for those rays departing from sources being interpolated.

A method is described for seismic imaging of the subsurface using dip-guided optimized stacking. The method computes weighting functions for a plurality of single-shot migrated images, unstacked seismic images, or partially stacked seismic images based on a slant stack performed using an input dip dataset; applying the plurality of weighting functions to the plurality of single-shot migrated images, unstacked seismic images, or partially stacked seismic images, or a plurality of dip-filtered images to create a plurality of weighted images; and summing the plurality of weighted images into a stacked seismic image. The method may be executed by a computer system.

A system and method can be used for to calibrating time-lapse seismic volumes by cross-migration rescaling and reorientation for use in determining optimal wellbore placement or production in a subsurface environment. Certain aspects include methods for cross-migration of data sets processed using different migration techniques. Pre-processing of the data sets, optimization of rescaling and reorientation, and identification of adjustment parameters associated with minimum global error can be used to achieve a time-dependent formation data set that addresses error in all input data sets.