Methods for inversion of seismic data to infer subsurface physical property parameters, comprising constructing an inhomogeneous anisotropy model and/or inhomogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model; and inverting the seismic data in a sequential or simultaneous approach to obtain at least one subsurface physical property parameter using an elastic inversion algorithm and the inhomogeneous anisotropy model and/or inhomogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model. Constructing an inhomogeneous anisotropy model may comprise deriving geobodies from at least one of seismic facies analysis, regional geologic information, or seismically derived earth models; and adjusting at least one of ε, δ, γ, or parameters of the elastic stiffness tensor matrix in a homogeneous anisotropy model in areas corresponding to the geobodies. Constructing an inhomogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model may comprise deriving geobodies and adjusting values in a homogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model in areas corresponding to the geobodies.

Systems and methods for determining mechanical properties of anisotropic media are disclosed. A method for determining mechanical properties of an anisotropic media includes obtaining log data of the anisotropic media, the log data corresponding to measurements of the anisotropic media collected with a logging tool; determining values for a plurality of first stiffness components of a stiffness matrix based on horizontal and vertical velocities derived from the log data; determining an upper bound for a second stiffness component of the stiffness matrix based on the values for the plurality of first stiffness components; estimating a value for the second stiffness component based on the determined upper bound; determining a mechanical property of the anisotropic media based on the estimated value of the second stiffness component; and providing the determined mechanical property.

A method for determining properties of a laminated formation traversed by a well or wellbore employs measured sonic data, resistivity data, and density data for an interval-of-interest within the well or wellbore. A formation model that describe properties of the laminated formation at the interval-of-interest is derived from the measured sonic data, resistivity data, and density data for the interval-of-interest. The formation model represents the laminated formation at the interval-of-interest as first and second zones of different first and second rock types. The formation model is used to derive simulated sonic data, resistivity data, and density data for the interval-of-interest. The measured sonic data, resistivity data, and density data for the interval-of-interest and the simulated sonic data, resistivity data, and density data for the interval-of-interest are used to refine the formation model and determine properties of the formation at the interval-of-interest. The properties of the formation may be a radial profile for porosity, a radial profile for water saturation, a radial profile for gas saturation, radial profile of oil saturation, and radial profiles for pore shapes for the first and second zones (or rock types).

An apparatus obtains measurements from a seismic sensor, wherein the seismic measurements include a set of seismic waves having at least a subset of seismic multiples and a machine-readable medium having program code executable by a processor to cause the apparatus to determine seismic measurements of the seismic waves, fit reflectivity model based on a set of reflectivity models using a nonlinear scheme to the seismic measurements, and identify a subset of the seismic measurements corresponding to the subset of seismic multiples. The apparatus also includes program code to cause the apparatus to generate a set of reduced-noise seismic measurements based on attenuation of the subset of the seismic measurements.

The subject disclosure relates to the interpretation of borehole sonic data using machine learning. In one example of a method in accordance with aspects of the instant disclosure, borehole sonic data is received, and machine learning is used to interpret the borehole sonic data.

Methods for determining reservoir characteristics of a well can include receiving a first core from the well; performing an experiment to determine the wave velocity associated with a first direction of the first core, the experiment including: transmitting an ultrasonic wave through the first core in the first direction; receiving the transmitted ultrasonic wave; and determining a directional wave velocity of the first core based on the transmitted ultrasonic wave and the received transmitted ultrasonic wave, wherein the directional wave velocity represents a wave velocity along the first direction; rotating the first core about a longitudinal axis of the first core; and performing the experiment along a second direction of the first core.

Methods are provided for determining properties of an anisotropic formation (including both fast and slow formations) surrounding a borehole. A logging-while-drilling tool is provided that is moveable through the borehole. The logging-while drilling tool has at least one dipole acoustic source spaced from an array of receivers. During movement of the logging-while-drilling tool, the at least one dipole acoustic source is operated to excite a time-varying pressure field in the anisotropic formation surrounding the borehole. The array of receivers is used to measure waveforms arising from the time-varying pressure field in the anisotropic formation surrounding the borehole. The waveforms are processed to determine a parameter value that represents shear directionality of the anisotropic formation surrounding the borehole.

Methods are provided for determining properties of an anisotropic formation (including both fast and slow formations) surrounding a borehole. A logging-while-drilling tool is provided that is moveable through the borehole. The logging-while drilling tool has at least one dipole acoustic source spaced from an array of receivers. During movement of the logging-while-drilling tool, the at least one dipole acoustic source is operated to excite a time-varying pressure field in the anisotropic formation surrounding the borehole. The array of receivers is used to measure waveforms arising from the time-varying pressure field in the anisotropic formation surrounding the borehole. The waveforms are processed to determine a parameter value that represents shear directionality of the anisotropic formation surrounding the borehole.

A method and a system for implementing the method are disclosed wherein the pre-stack seismic input data, an initial anellipticity anisotropy parameter, and a baseline normal moveout velocity from a non-flat surface, are sometimes mild or foothill topography as well as the shot and receiver lines might not necessarily be straight, and often curve to avoid obstacles on the land surface. In particular, the method and system disclosed, allows for updating the anisotropy parameters iteratively and when the stopping criteria is satisfied, the final estimated parameter can be directly used for time migration. This method and system are mainly used for time migration with the purpose of obtaining the high fidelity (accurate amplitude, i.e. not only travel-time correct but also amplitude correct) image gathers which are used for reservoir characterization and interpretation.

Disclosed are systems and methods for selecting modes and frequencies of interest in a slowness-frequency map of sonic logging information. These include measuring, by a sonic logging tool, sonic data within a borehole, determining a frequency range for a selected mode of the sonic data, determining a slowness range for the selected mode of the sonic data, applying the frequency range and the slowness range to the sonic data to select a subset of data from the sonic data, processing the selected subset of data.