A system includes a first instrumented bridge plug positionable in a downhole wellbore environment. The first instrumented bridge plug includes an acoustic source for transmitting an acoustic signal. The system also includes a second instrumented bridge plug positionable in the downhole wellbore environment. The second instrumented bridge plug includes an acoustic sensor for receiving a reflected acoustic signal originating from the acoustic signal. The reflected acoustic signal being usable to interpret wellbore formation characteristics of the downhole wellbore environment.

A system includes a first instrumented bridge plug positionable in a downhole wellbore environment. The first instrumented bridge plug includes an acoustic source for transmitting an acoustic signal. The system also includes a second instrumented bridge plug positionable in the downhole wellbore environment. The second instrumented bridge plug includes an acoustic sensor for receiving a reflected acoustic signal originating from the acoustic signal. The reflected acoustic signal being usable to interpret wellbore formation characteristics of the downhole wellbore environment.

Methods and systems for identifying a pay zone in a subterranean formation can include: logging a well extending into the subterranean formation including measuring bulk density, compressional wave travel time and shear wave travel time at different depths in the subterranean formation; calculating elastic attributes including acoustic impedance and compressional velocity-shear velocity ratio at different depths in the subterranean formation; and displaying and analyzing the calculated elastic attributes to identify the low resistivity pay zones.

Methods and systems for identifying a pay zone in a subterranean formation can include: logging a well extending into the subterranean formation including measuring bulk density, compressional wave travel time and shear wave travel time at different depths in the subterranean formation; calculating elastic attributes including acoustic impedance and compressional velocity-shear velocity ratio at different depths in the subterranean formation; and displaying and analyzing the calculated elastic attributes to identify the low resistivity pay zones.

Examples described herein provide a computer-implemented method that includes analyzing a first dataset by applying the first dataset to a first model to generate a first result. The method further includes analyzing a second dataset by applying the second dataset to a second model to generate a second result. The method further includes performing validation on the first model and the second model by comparing the first result to the second result. The method further includes, responsive to determining that the first result and the second result match, modifying an operational action of a surface assembly based on at least one of the first result or the second result. The method further includes, responsive to determining that the first result and the second result do not match, updating at least one of the first model or the second model.

Examples described herein provide a computer-implemented method that includes analyzing a first dataset by applying the first dataset to a first model to generate a first result. The method further includes analyzing a second dataset by applying the second dataset to a second model to generate a second result. The method further includes performing validation on the first model and the second model by comparing the first result to the second result. The method further includes, responsive to determining that the first result and the second result match, modifying an operational action of a surface assembly based on at least one of the first result or the second result. The method further includes, responsive to determining that the first result and the second result do not match, updating at least one of the first model or the second model.

A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

To generate dispersion curves for acoustic waves in a radially layered system, a matrix M containing solutions to the wave equation subject to the boundary conditions of the system is constructed. The reciprocal condition number (RCN) of the matrix M is determined as a function of acoustic wave frequency and slowness. The local minima of the RCN in the frequency-slowness plane produces the dispersion curves corresponding to allowable acoustic modes in the system. A sensitivity analysis which identifies the dispersion curves dependent on a selected parameter. The dispersion curves independent of the perturbed parameters are eliminated by perturbing the modeling parameters and generating the RCN of the perturbed matrix M and then subtracting the RCN values of the unperturbed matrix M, leaving the dispersion curves that exhibit dependence on the selected parameter.

To generate dispersion curves for acoustic waves in a radially layered system, a matrix M containing solutions to the wave equation subject to the boundary conditions of the system is constructed. The reciprocal condition number (RCN) of the matrix M is determined as a function of acoustic wave frequency and slowness. The local minima of the RCN in the frequency-slowness plane produces the dispersion curves corresponding to allowable acoustic modes in the system. A sensitivity analysis which identifies the dispersion curves dependent on a selected parameter. The dispersion curves independent of the perturbed parameters are eliminated by perturbing the modeling parameters and generating the RCN of the perturbed matrix M and then subtracting the RCN values of the unperturbed matrix M, leaving the dispersion curves that exhibit dependence on the selected parameter.