A method and system for estimating a full elastic tensor. The method may comprise taking a measurement for compressional wave sonic data and cross-dipole shear data with a sonic logging tool at a first location as cross-dipole data, processing the compressional wave sonic data to produce a compressional wave slowness (P), and processing the cross-dipole shear data to produce a fast horizontal polarized shear wave slowness (SH) and a slow quazi-vertical shear wave slowness (qSV) as a function of depth. The method may further comprise setting an initial guess for at least five constants of the full elastic tensor for Vertical Transversely Isotropy (VTI) symmetry, determining a modeled slowness surfaces from the full elastic tensor, and comparing the modeled slowness surfaces with measured values of the P, the SH, and the qSV. The method may be performed by a system comprising a sonic logging tool and an information handling system.

A method of predicting three-dimensional fracture geometry in a subterranean region of interest is disclosed. The method includes obtaining a strain tensor for the subterranean region of interest, calculating a set of principal strain components from the strain tensor, and determining a strain cyclide from the set of principal strain components. The method further includes calculating a set of quadrimodal fault normal vectors from the strain cyclide and determining an in-plane shear strain magnitude and a shear strain orientation from the set of quadrimodal fault normal vectors.

Some systems and methods of hydraulic fracturing a formation of a borehole include receiving a length-to-radius ratio of a borehole segment of the borehole and determining when the length-to-radius ratio is less than a threshold. Responsive to determining that the length-to-radius ratio is less than the threshold, some systems and methods include predicting a breakdown pressure associated with a formation surrounding the borehole segment based on a length of the borehole segment. Responsive to determining that the length-to-radius ratio is greater than or equal to the threshold, some systems and methods include determining, a characteristic diffusion time associated with a fluid diffusing into the formation surrounding the borehole segment. Some systems and methods include pumping the fluid into the borehole segment to fracture the formation surrounding the borehole segment at the determined breakdown pressure.

A method and system for modeling a subsurface region include applying a trained machine learning network to an initial petrophysical parameter estimate to predict a geologic prior model; and performing a petrophysical inversion with the geologic prior model, geophysical data, and geophysical parameters to generate a rock type probability model and an updated petrophysical parameter estimate. Embodiments include managing hydrocarbons with the rock type probability model. Embodiments include checking for convergence of the updated petrophysical parameter estimate; and iteratively: applying the trained machine learning network to the updated petrophysical parameter estimate of a preceding iteration to predict an updated rock type probability model and another geologic prior model; performing a petrophysical inversion with the updated geologic prior model, geophysical seismic data, and geophysical elastic parameters to generate another rock type probability model and another updated petrophysical parameter estimate; and checking for convergence of the updated petrophysical parameter estimate.

A method includes receiving a first transition probability matrix (TPM) of a subsurface region, wherein the TPM defines, for a given lithology at a current depth sample (or micro-layer), a probability of particular lithologies at a next depth sample (or micro-layer), receiving seismic data for the subsurface region, utilizing the first TPM and the seismic data to generate first pseudo wells, calculating a second TPM from the first pseudo wells, determining whether the second TPM is consistent with the first TPM, and utilizing the first pseudo wells to characterize a reservoir in the subsurface region when the second TPM is determined to be consistent with the first TPM.

A method includes receiving information for a subsurface region; based at least in part on the information, identifying sub-regions within the subsurface region; assigning individual identified sub-regions a dimensionality of a plurality of different dimensionalities that correspond to a plurality of different models; via a model-based computational framework, generating at least one result for at least one of the individual identified sub-regions based at least in part on at least one assigned dimensionality; and consolidating the at least one result for multiple sub-regions.

Method for characterizing subterranean formation is described. One method involves simulating a poroelastic pressure response of known fracture geometry utilizing a geomechanical model to generate a simulated poroelastic pressure response. Compiling a database of simulated poroelastic pressure responses. Measuring a poroelastic pressure response of the subterranean formation during a hydraulic fracturing operation to generate a measured poroelastic pressure response. Identifying a closest simulated poroelastic pressure response in the library of simulated poroelastic pressure response. Estimating a geometrical parameter of a fracture or fractures in the subterranean formation based on the closest simulated poroelastic pressure response.

A method that includes obtaining log data of a downhole formation, and characterizing the downhole formation by determining stiffness coefficients including C.sub.33, C.sub.44, C.sub.66, C.sub.11, C.sub.12, and C.sub.13. C.sub.13 is a function of C.sub.33, C.sub.44, C.sub.66, and at least one of a kerogen volume and a clay volume derived from the log data. In another method or system, C.sub.13 is derived based at least in part on C.sub.11 calculated as C.sub.11=k.sub.1[C.sub.33+2(C.sub.66−C.sub.44)]+k.sub.2 or C.sub.33 calculated as C.sub.33=((C.sub.11−k.sub.2)/k.sub.1)−2(C.sub.66−C.sub.44), where k.sub.1 and k.sub.2 are predetermined constants. In another method or system, C.sub.13 is derived in part from at least one of a kerogen volume derived from the log data, a clay volume derived from the log data, C.sub.11 calculated as C.sub.11=k.sub.1[C.sub.33+2(C.sub.66−C.sub.44)]+k.sub.2, or C.sub.33 calculated as C.sub.33=((C.sub.11−k.sub.2)/k.sub.1)−2(C.sub.66−C.sub.44), where k.sub.1 and k.sub.2 are predetermined constants.

A technique facilitates estimating elastic properties of formations by exciting a wavefield and acquiring the signal with and without azimuthal decompositions. For example, the elastic properties may be estimated by exciting a multipole wavefield and acquiring the signal with and without the azimuthal decomposition. The technique is effective for estimating elastic properties of azimuthally homogeneous and heterogeneous formations including isotropic and anisotropic formations.

A method for determining an intersection between a polygon representing a boundary of a surface in an earth formation and a plane includes: receiving a polygon representing a boundary of a surface in an earth formation, the polygon having a series of straight segments with a point at each end of each of the segments; overlaying a cutting grid having grid planes over the polygon; identifying a specific pattern of two adjacent segments in the polygon by proceeding from a first segment to a second segment in a selected rotational direction; matching the specific pattern to a reference pattern; categorizing the point between the two adjacent segments as an intersection point or as a non-intersection point based on the reference pattern; the iterating the identifying, matching, and categorizing for each pair of adjacent segments in the polygon such that each point between adjacent segments in the polygon is categorized.