Abstract: A method includes receiving information that includes elastic property information and that includes sonic data acquired via a tool disposed at a plurality of depths in a bore in a subterranean environment that includes at least one anisotropic formation; processing the information to generate processed information where the processed information includes variance information associated with the elastic property information and where the processed information includes velocity information and orientation information associated with the sonic data; performing an inversion based at least in part on the processed information; and outputting values for elastic parameters based at least in part on the inversion.

Abstract: Aspects provide for methods that successfully evaluates multiple compressional and shear arrival events received by a sonic logging tool to evaluate the presence of structures, such as shoulder beds, in downhole environments. In particular, the methods described herein enable automated determination of properties of laminated reservoir formations by, for example, enabling the automated determination of arrival times and slownesses of multiple compressional and shear arrival events received by a sonic logging tool.

Abstract: Sonic logging data including a sonic waveform associated with a plurality of shot gathers is accessed. A transformation operator is applied to the sonic logging data to provide a transformed sonic image, the transformation operator including at least one of a short time average long time average (STA/LTA) operator, a phase shift operator, and a deconvolution operator. A machine learning process is performed using the transformed sonic image to determine a sonic slowness associated with the sonic logging data. The sonic slowness is provided as an output.

Abstract: Sonic logging data including a sonic waveform associated with a plurality of shot gathers is accessed. A transformation operator is applied to the sonic logging data to provide a transformed sonic image, the transformation operator including at least one of a short time average long time average (STA/LTA) operator, a phase shift operator, and a deconvolution operator. A machine learning process is performed using the transformed sonic image to determine a sonic slowness associated with the sonic logging data. The sonic slowness is provided as an output.

Abstract: Methods arc provided for using sonic tool data to investigate a multi-string wcllbore. The sonic data is processed to obtain indications of phase slowness dispersions for multiple locations in the wellbore. The dispersions are aggregated. The aggregated dispersions are compared with a plurality of cut-off mode templates to identify the presence of cut-off modes or the lack thereof in the aggregated phase slowness dispersions. Features of the multi-string wellbore are identified based on the presence of the cut-off modes or the lack thereof. In another method, the sonic data is processed to obtain indications as a function of depth of at least one of an energy spectrum, a semblance projection, a slowness dispersion projection, an attenuation dispersion projection, and a wavenumber dispersion projection. The indications are inspected to locate a shift at a particular depth indicat- ing a transition in at least oneannulus of the multi-string wellbore.

Abstract: A method includes receiving information that includes elastic property information and that includes sonic data acquired via a tool disposed at a plurality of depths in a bore in a subterranean environment that includes at least one anisotropic formation; processing the information to generate processed information where the processed information includes variance information associated with the elastic property information and where the processed information includes velocity information and orientation information associated with the sonic data; performing an inversion based at least in part on the processed information; and outputting values for elastic parameters based at least in part on the inversion.

Abstract: A method for estimating formation slowness is provided. The method comprises forward modeling to compute formation slownesses based on a first method for orthorhombic media using stress magnitudes and third-order elastic constants as inputs, and forward modeling to determine formation slownesses analytically based on a second method using stress magnitudes, stress azimuth and third-order elastic constants as inputs. The first method may be based on Tsvankin method and the second method may be based on Christoffel method. The forward modeling may further use well configuration and reference moduli as inputs, and the results from the forward modeling may include formation slownesses, and at least one of vertical slownesses, anisotropic parameters, anellipticity indicators and fast shear azimuth. The method may further comprise assessing quality of the forward modeling based on results output from the forward modeling.

Abstract: Methods arc provided for using sonic tool data to investigate a multi-string wcllbore. The sonic data is processed to obtain indications of phase slowness dispersions for multiple locations in the wellbore. The dispersions are aggregated. The aggregated dispersions are compared with a plurality of cut-off mode templates to identify the presence of cut-off modes or the lack thereof in the aggregated phase slowness dispersions. Features of the multi-string wellbore are identified based on the presence of the cut-off modes or the lack thereof. In another method, the sonic data is processed to obtain indications as a function of depth of at least one of an energy spectrum, a semblance projection, a slowness dispersion projection, an attenuation dispersion projection, and a wavenumber dispersion projection. The indications are inspected to locate a shift at a particular depth indicat- ing a transition in at least oneannulus of the multi-string wellbore.

Abstract: A computer-implemented method for determining elastic properties for a heterogeneous anisotropic geological formation is described herein. The method includes grouping sonic velocity data from a borehole section (or borehole sections) into a number of clusters (e.g., one or more clusters). The sonic velocity data is grouped into clusters using petrophysical log data from the borehole section. The method also includes inverting the sonic velocity data for the clusters to determine elastic properties for each cluster. In some cases, the elastic properties for the clusters are combined to determine a relationship between the elastic properties and formation heterogeneity.

Abstract: A method for estimating formation slowness is provided. The method comprises forward modeling to compute formation slownesses based on a first method for orthorhombic media using stress magnitudes and third-order elastic constants as inputs, and forward modeling to determine formation slownesses analytically based on a second method using stress magnitudes, stress azimuth and third-order elastic constants as inputs. The first method may be based on Tsvankin method and the second method may be based on Christoffel method. The forward modeling may further use well configuration and reference moduli as inputs, and the results from the forward modeling may include formation slownesses, and at least one of vertical slownesses, anisotropic parameters, anellipticity indicators and fast shear azimuth. The method may further comprise assessing quality of the forward modeling based on results output from the forward modeling.

Abstract: A computer-implemented method for determining elastic properties for a heterogeneous anisotropic geological formation is described herein. The method includes grouping sonic velocity data from a borehole section (or borehole sections) into a number of clusters (e.g., one or more clusters). The sonic velocity data is grouped into clusters using petrophysical log data from the borehole section. The method also includes inverting the sonic velocity data for the clusters to determine elastic properties for each cluster. In some cases, the elastic properties for the clusters are combined to determine a relationship between the elastic properties and formation heterogeneity.