Predicting elastic parameters of a subsurface includes modelling changes in the shear modulus and changes in the bulk modulus of the subsurface as a combination of a host medium shear modulus and host medium bulk modulus and a plurality of inclusion shear moduli and inclusion bulk moduli. Each inclusion shear modulus and inclusion bulk modulus associated with a unique inclusion geometry. An inclusion-based rock physical model is used to solve the models for changes in shear modulus and changes in bulk modulus to predict an effective shear modulus of the subsurface and an effective bulk modulus of the subsurface.

Computing device, computer instructions and method for estimating a broadband wavelet associated with a given seismic data set. The method includes receiving broadband seismic data; constructing and populating a misfit function; calculating the broadband wavelet based on the misfit function and the broadband seismic data; and estimating physical reservoir properties of a surveyed subsurface based on the broadband wavelet. The broadband wavelet is constrained, through the misfit function, by (1) an amplitude only long wavelet, and (2) an amplitude and phase short wavelet. The amplitude and phase short wavelet is shorter in time than the amplitude only long wavelet.

A method for estimating a fluid pressure required to stimulate a subsurface formation includes using seismic signals detected by a plurality of seismic sensors disposed proximate the subsurface formation. Spatial positions and times of origin (“hypocenters”) of each of a plurality of microseismic events induced by pumping fluid into the subsurface formation are estimated. Magnitudes and directions of principal stresses are estimated from the hypocenters and from amplitude and phase of the detected seismic signals for each of the microseismic events. Shear and normal stresses of induced fractures are from the estimated principal stresses. A fluid pressure required to cause formation failure on each fracture is estimated using the estimated shear and normal stresses.

A method may include obtaining core image data regarding a geological region of interest. The method may further include obtaining well log data regarding the geological region of interest from one or more wells. The method may further include determining a sliding window that corresponds to a predetermined window size. The method may further include determining various quantitative image attributes using the core image data, the well log data, and the sliding window. The quantitative image attributes may be determined in a continuous manner by moving the sliding window along the core image data. The method may further include generating predicted rock data for the geological region of interest using the quantitative image attributes, a machine-learning algorithm, and a machine-learning model.

Method for characterizing subterranean formation is described. One method involves injecting a fluid into an active well of the subterranean formation at a pressure sufficient to induce one or more hydraulic fractures. Measuring, via a pressure sensor, a poroelastic pressure response caused by inducing of the one or more hydraulic fractures. The pressure sensor is in at least partial hydraulic isolation with the one or more hydraulic fractures.

An apparatus and method of using drilling vibrations generated by the deformation of a rock formation in response to forces acting on the rock formation, where the forces are related to a drill bit and/or drilling fluid system, to identify the nature and occurrence of fractures, fracture swarms and other mechanical discontinuities (boundaries) such as bedding planes and/or faults that offset or otherwise separate rock formations with different mechanical rock properties.

A method of seismic modeling using an elastic model, the elastic model including a grid having a grid spacing sized such that, when synthetic seismic data is generated using the elastic model, synthetic shear wave data exhibits numerical dispersion, the method including: generating generated synthetic seismic data using the elastic model, wherein the generated synthetic seismic data includes synthetic compression wave data and synthetic shear wave data, and modifying the generated synthetic seismic data to produce modified synthetic seismic data by attenuating at least some of the synthetic shear wave data in order to attenuate at least some of the numerically dispersive data.

A Gaussian process is used to provide a nonparametric approach for modeling nonlinear relationships among physical quantities involved in the geomechanics workflow supporting drilling & completion operations. Gaussian process provides a nonparametric framework that enables injection of a prior belief into the basic model format while allowing its specific format to be adaptive in a certain range following an estimated distribution. Both this model-related uncertainty and the pre-assumed input data distributions may be calibrated using non-parametric Bayesian framework with Gaussian process as prior. This approach not only the addresses the uncertainty stemming from the input physical properties but also tackles the uncertainties underlying the adopted physical model, all in this nonparametric Bayesian framework with Gaussian process encoded as prior.

Partially coupling a geomechanical simulation with a reservoir simulation facilitates predicting strain behavior for a reservoir from production and injection processes. A method comprises generating a geomechanical model based on a mechanical earth model that represents a subsurface area. The geomechanical model indicates a division of the mechanical earth model into a plurality of grid cells that each correspond to a different volume of the subsurface area. Based on a first virtual compaction experiment with the geomechanical model, compaction curves are generated. The compaction curves represent porosity as a function of stress. The compaction curves are converted from porosity as a function of stress to porosity as a function of pore pressure. The geomechanical model is partially coupled to a reservoir simulation model using the converted compaction curves.

An interconnect structure includes an interconnect structure includes an etching stop layer; a dielectric layer and an insert layer on the etching stop layer, and a conductive feature in the dielectric layer, the insert layer and the etching stop layer. A material of the insert layer is different from the dielectric layer and the etching stop layer.