Systems and methods for modeling dispersion curves are disclosed. The method includes obtaining an acoustic dataset along a well that accesses a hydrocarbon reservoir. The method further includes determining a set of depth windows along the well and determining a first subset of dispersion curves for a first subset of depth windows using a dispersion model. The method still further includes initializing a second subset of dispersion curves for a second subset of depth windows using a nearest neighbor search of the first subset of dispersion curves. The method still further includes determining slowness-frequency pairs for the second subset of depth windows using the acoustic dataset and updating the second subset of dispersion curves using a recursive scanning method. The method still further includes characterizing rock properties near the well based, at least in part, on the first subset of dispersion curves and the second subset of dispersion curves.

A method is provided for automatically classifying grains, pores, or both of a formation sample. The method includes receiving a digital image representation of the formation sample, and identifying a plurality of pores, grains, or both in the digital image representation. The method also includes computing a plurality of geometric features associated with the pores, grains, or both in the digital image representation, and inputting the geometric features into an unsupervised machine learning model. The unsupervised machine learning model determines a label for each identified pore and grain, the label being a pore-type or a grain-type, and the plurality of geometric features and the labels determined for each pore, grain, or both, are input into a supervised machine learning model. The supervised machine learning model determines a final classification of a pore-type for each pore and a grain-type for each grain in the digital image representation of the formation sample.

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 is described for seismic amplitude analysis including receiving a seismic dataset representative of a subsurface volume of interest wherein the seismic dataset includes an angle or angle stack dimension; select a plurality of sets of sub-cubes in the seismic dataset wherein each set of sub-cubes includes a plurality of the angles or the angle stacks; compute standard score statistics for each of the plurality of sub-cubes; identify amplitude variation with angle (AVA) anomalies based on the standard score statistics for each of the set of sub-cubes; classify the AVA anomalies to generate classified AVA anomalies; and displaying, on a user interface, the classified AVA anomalies as a graphical display. The method is executed by a computer system.

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.

Methods, systems, and devices for evaluating an earth formation intersected by a borehole using information from standard resolution measurements. Methods include generating an image representative of the formation over an interval of borehole depth, the image having a second resolution greater than the first resolution. Generating the image may be carried out by identifying layers corresponding to lithotype facies within the interval, the layers defined by boundaries having boundary locations along the borehole; and using a unified characterization of the formation within the interval determined from the standard resolution measurements and the boundary locations within the interval to solve for a value for the formation parameter corresponding to each layer consistent with the unified characterization of the interval. The unified characterization may be an average value for the formation parameter within the interval.

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.

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.