The estimation of the elastic properties of the crust from surface seismic recordings is of great importance for an understanding of the lithology and the detection of mineral resources. Although in marine reflection experiments only P waves are recorded, information on shear properties of the medium is contained in the reflection amplitudes recorded at different distances from the source. Being able to estimate both dilatational and shear properties gives stronger constraints on the lithology. It is therefore desirable to recover both types of elastic parameters from multi-offset seismograms. In the realdata example presented here, the amplitude of the reflections cannot be explained by one parameter related to the dilatational properties ( P impedance) only, when trying to minimize the least-squares fit between synthetic and real multi-offset seismograms.When adding a n additional parameter related to the shear properties (Poisson's ratio), the fit between synthetic and real seismograms improves. Synthetic-waveform-fitting experiments underline the possibility of recovering Poisson's ratio when the P-impedance model is well known from fitting waveforms recorded a t small offsets. In the real-data example, the resulting models for P impedance and Poisson's ratio are anticorrelated in most depth regions, but are correlated in a particular depth region, indicating a sudden change in lithology.
Estimation of the elastic properties of the crust from surface seismic recordings is of great importance for the understanding of lithology and for the detection of mineral resources. Although in marine reflection experiments only P-waves are recorded, information on shear properties of the medium is contained in multioffset reflection seismograms. Being able to retrieve both dilatational and shear properties gives stronger constraints on the lithology. It is therefore desirable to recover isotropic elastic parameters from multioffset seismograms. Unfortunately, most classical waveform fitting methods used for extracting shear properties of the subsurface are based on a 1-D earth model assumption and on linear approximations of the wave equations. In this paper, a 2.5-D elastic waveform inversion method is used to extract the variations of acoustic impedance and Poissonâs ratio from marine multioffset reflection seismograms collected in the Gulf of Mexico area. A complete seismic profile is interpreted, including complex physical phenomena apparent in the data, such as unconsolidated sediment reflections and seismic refraction events. The amplitude of the reflections cannot be explained by one parameter related to the dilatational properties (P-impedance) only, when trying to minimize the least absolute fit between observed and synthetic seismograms. When adding an additional parameter related to shear properties (Poissonâs ratio), the fit between observed and synthetic seismograms improves. The resulting 2-D models of P-impedance and Poissonâs ratio contrasts are anticorrelated almost everywhere in depth, except where hydrocarbons are present. The estimation of physical P-impedance and Poissonâs ratio models by a full waveform fitting allows lithology characterization and, therefore, the delineation of a shaleâoverâgas sand reservoir.
A Bayesian methodology is described for designing experiments or surveys that will optimally complement all previously available information. This methodology uses strong prior information to linearize the problem, and to guide the design toward maximally reducing forecast uncertainties in the interpretation of the future experiment. The prior information could possibly be correlated among model parameters or the observation noise. With no prior information this approach reduces to the fast recursive implementation of the D-optimality criterion. Synthetic geophysical tomography examples are used to illustrate the benefits of this approach. The dependence of the design on the model representation is also discussed.
The locations of seismic events are used to infer reservoir properties and to guide future production activity, as well as to determine and understand the stress field. Thus, locating seismic events with uncertainty quantification remains an important problem. Using Bayesian analysis, a joint probability density function of all event locations was constructed from prior information about picking errors in kinematic data and explicitly quantified velocity model uncertainty. Simultaneous location of all seismic events captured the absolute event locations and the relative locations of some events with respect to others, along with their associated uncertainties. We found that the influence of an uncertain velocity model on location uncertainty under many realistic scenarios can be significantly reduced by jointly locating events. Many quantities of interest that are estimated from multiple event locations, such as fault sizes and fracture spacing or orientation, can be better estimated in practice using the proposed approach.
SUMMARYWe study the problem of determining an unknown microseismic event location relative to previously located events using a single monitoring array in a monitoring well. We show that using the available information about the previously located events for locating new events is advantageous compared to locating each event independently. By analyzing confidence regions, we compare the performance of two previously proposed location methods, double-difference and interferometry, for varying signal-to-noise ratio and uncertainty in the velocity model. We show that one method may have an advantage over another depending on the experiment geometry, assumptions about uncertainty in velocity and recorded signal, etc. We propose a unified approach to relative event location that includes double-difference and interferometry as special cases, and is applicable to velocity models and well geometries of arbitrary complexity, producing location estimators that are superior to those of double-difference and interferometry.
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