Techniques for generating a geological model from 3D seismic data and rock property data are disclosed. Rock property data and 3D seismic data are received. Based on the rock property data and the 3D seismic data, an adaptive geological model is generated. The adaptive geological model includes a characteristic geological property. Synthetic seismic data is generated from a first region of interest of the adaptive geological model. The synthetic seismic data is adapted to facilitate a comparison between the first region of interest and a corresponding region of interest of the received 3D seismic data. The characteristic geological property is adjusted until the comparison indicates a result that is within a predetermined threshold region of the corresponding value from the rock properties. A validated geologic model is then generated.

Scattered body waves are isolated to primary, shear, and surface waves as a receiver wavefield from recorded near-surface scattered wave data generated by scatters. The isolated receiver wavefield is backward propagated through an earth model from a final to an initial state. A source wavefield and the receiver wavefields are cross-correlated. A source wavefield and the receiver wavefields are stacked, over all time steps and sources, to generate a subsurface image. A display of the subsurface image is initiated.

System and methods for predicting time-dependent rock properties are provided. Seismic data for a subsurface formation is acquired over a plurality of time intervals. A value of at least one rock property of the subsurface formation is calculated for each of the plurality of time intervals, based on the corresponding seismic data acquired for that time interval. At least one of a trend or a spatio-temporal relationship in the seismic data is determined based on the value of the at least one rock property calculated for each time interval. A value of the at least one rock property is estimated for a future time interval, based on the determination. The estimated value of the at least one rock property is used to select a location for a wellbore to be drilled within the subsurface formation. The wellbore is then drilled at the selected location.

A computer-implemented method and computing system apparatus programmed to perform operations of the computer-implemented method for obtaining a subsurface stack image, subsurface angle gathers, and a subsurface velocity model over an entire survey region having high velocity contrast geo-bodies. Particularly, user inputs, input velocity models, and surface-seismic data are obtained by fixed source and receiver pairs and then used by the computer program product embedded within the computing system apparatus to minimize the number of iterations, required to obtain a final velocity model, a final stack image, and final angle gathers wherein their flatness deviation is equal to, or less than, a user-defined flatness value. Therefore, the attributes developed by said computer-implemented method and system can help solve the imaging problem of sub high velocity contrast geo-bodies like subsalt, or salt overhung deep mini basins.

Hydrocarbon exploration and extraction can be facilitated by determining fault surfaces from fault attribute volumes. For example, a system described herein can receive a fault attribute volume for faults in a subterranean formation determined using seismic data. The fault attribute volume may include multiple traces with trace locations. The system can determine a set of fault samples for each trace location. Each fault sample can include fault attributes such as a depth value, an amplitude value, and a vertical thickness value. The system can determine additional fault attributes such as a dip value and an azimuth value for each fault sample of each trace location. The system can determine fault surfaces for the faults using the fault samples and fault attributes. The system can then output the fault surfaces for use in a hydrocarbon extraction operation.

The present invention discloses a physical embedded deep learning formation pressure prediction method, device, medium and equipment, the present invention characterizes seismic attenuation by logging impedance quality factor Q, based on the Q value and rock physics model of formation pressure, the physical mechanism of this kind of certainty replace Caianiello convolution neurons of the nonlinear activation function, using the convolution neurons, build deep learning convolution neural networks (CCNNs), can greatly increase the stress inversion precision and learning efficiency, get accurate formation pressure prediction results. Compared with the prior art, the present invention uses acoustic attenuation instead of the traditional acoustic velocity to characterize formation pressure, and solves the problem that the traditional pressure prediction method based on velocity has strong multiple solutions due to high gas content and complex structure.

Computer-implemented stratigraphic play quality generation is disclosed. Stratigraphic data can be processed from each of a plurality of respective data sources to generate conditioned stratigraphic data. From at least some of the conditioned stratigraphic data, attributes of at least one seismic sequence can be extracted, and at least one seismic surface and at least one structural element associated with at least some of the conditioned stratigraphic data can be determined. At least some of the conditioned stratigraphic data representing sedimentary layers can be correlated with seismic reflection data to ascertain a subsurface of the geologic area at a respective depth. Reservoir properties associated with the geologic area are linked to elastic properties, and a 2D model built. Moreover, 3D map can be generated that is usable for a prospective drilling plan.

Downhole measurement systems and methods include deploying a bottomhole assembly having a multipole transmitter into a formation and transmitting acoustic signals into the formation. The multipole transmitter is of order n ≥ 2. Acoustic signals are received at respective receivers that are circumferentially aligned with the multipole transmitter, and are axially offset from the multipole transmitter, and axially offset from each other. The order of the first and second multipole receivers are equal to the order of the multipole transmitter. A controller is used to obtain first and second acoustic multipole data from the first and second multipole receivers at one or more azimuthal angles of a rotation of the bottomhole assembly in a formation during a drilling operation. Acoustic azimuthal anisotropy of the formation is determined from the first acoustic multipole data and the second acoustic multipole data.

A method for modelling and migrating seismic data, that includes using an acoustic wave equation and a spatial distribution of one or more earth-model parameters. The acoustic wave equation is modified by including at least one secondary source term, and based on a seismic acquisition configuration, either calculating the seismic signals that would be detected from the modelled wavefield or migrating observed seismic signals or migrating residual signals as part of an inversion.

Methods and systems, including computer programs encoded on a computer storage medium are described for generating data used to simulate properties of a target area in a subterranean region. A system obtains data describing a water-hydrocarbon interface of the target area and selects control parameters for processing the data based on a formation mechanism corresponding to a pressure or temperature of the target area. An objective function is determined that measures a delta between first values observed in the data and second values from simulations of the target area. Based on outputs of the function, the system calculates values for the control parameters that minimizes the delta between the first values observed in the data and the second values from simulations of the target area. The input data is generated to simulate properties of the target area based on calculated values of the control parameters.