Ultrasonic guided waves are widely used in the fields of Non-Destructive Evaluation (END) and Structural Health Monitoring (SHM), allowing the inspection of structures and pieces of equipment in a non-invasive manner. Through the transmission of an acoustic signal over a given object and the acquisition of the signal from the propagated waves using a group of sensors in predefined positions, it is possible to obtain material information regarding the investigated structure. In the Oil & Gas industry, the use of this type of wave is integral to the logging of the cement layer that outlines the walls of wellbores, which has the purpose of guaranteeing structural support and protecting the well’s internal production structure and the surrounding groundwater from each other. During the deactivation and abandonment of a production well, it is necessary to evaluate the hydraulic isolation of the cement layer, as well as identify possible defects. The propagation of guided waves in a structure is usually multi-modal and of dispersive characteristic. The latter means that the propagating waves phase velocity is dependent on the frequency, translating into a non-linear relationship between wavenumber and frequency. This dispersion relation contains information about the propagating medium, such as elastic constants and dimensions, and can be represented as curves in the frequency-wavenumber (f-k) plane. Different methods are currently being explored for obtaining the dispersion relation from time-domain signals acquired by ultrasonic sensors in different spatial positions. This work explored three different methods for the extraction of the dispersion curves, that is, obtaining the f-k points associated with the modes of propagation, from a dataset composed of space-time signals. The first algorithm is based on a pre-existing technique that uses the bidimensional Fourier Transform (2-D FT) over the matrix containing the space-time signals from the ultrasonic sensors, generating an f-k matrix whose local maximas correspond to points belonging to dispersions curves. The representation of the matrix as an f-k image shows the dispersion curves as contiguous groups of pixels with elevated brightness. A new algorithm is proposed, based on morphological operations from image-processing, to identify the pixels relative to the f-k points of the dispersion curves in the image, after pre-processing is performed. The second technique consists of pre-processing the same fk image, obtained from the 2-D FT, and the use of an existing algorithm for the detection of curvilinear structures in images to identify the points corresponding to the f-k curves. The third method proposes the adaptation of an existing method of estimation of the wavenumbers associated with the dispersion curves for different frequencies, using a matrix Pencil. This work also proposes an original algorithm to separate the f-k points, retrieved by the three techniques, in different curves associated with each mode of propagation. The algorithms used here for the estimation of the dispersion curves are evaluated over three distinct datasets of finite elements simulation: a thin aluminum plate under different values of axial traction parallel to the direction of propagation of the waves; a multilayer wellbore without tubing, with different types of cement defects-channeling, low cement quality, internal and external decoupling-, and without defect; a multilayer wellbore with tubing with the same cement defects and with no defect. Finally, a comparison is drawn over the capacity of the extraction algorithms of providing information regarding changes in the material qualities of the simulated objects. The work also evaluates the precision and computational performance of the aforementioned algorithms.