1,721,236 research outputs found
Applications of seismic AVA inversions for petrophysical characterization of subsurface targets
No full textWe illustrate the estimation of the spatial distribution of porosity, saturation, and shaliness for two gas sand reservoirs, one hosted in a complex channel system and the other in a sequence of turbiditic sandstones, directly from the seismic observations. To this end, we employ seismic AVA inversion methods in which the quantitative relations between elastic and petrophysical properties are directly included in the forward modeling of the inversion kernel. These inversion approaches have recently become standard tools in reservoir characterization studies. We discuss a target-oriented method, where we consider the AVA response of the interface between the cap rock and the reservoir, and two interval-oriented approaches, where we invert the angle traces in a time interval that includes the reservoir layers. In solving the inverse problem, we make use of either analytical equations or numerical MCMC algorithms. All the methods are cast in a Bayesian framework so that we estimate both the most likely solutions and the associated uncertainties
Petrophysical inversion of AVA data
No full textWe investigate the interactions between the elastic parameters, Vp, Vs and density, estimated by non linear inversion of AVA data and the petrophysical parameters, depth (pressure), porosity, clay content and fluid saturation, of an actual gas-bearing reservoir. In particular, we study how the ambiguous solutions derived from the non-uniqueness of the seismic inversion affect the estimates of relevant rock properties. It turns out that the physically admissible values of the rock properties greatly reduce the range of possible seismic solutions and this range neatly encloses the actual values given by the well.
Then, by means of a statistical inversion, we analyse how an approximate a-priori knowledge of the petrophysical properties and of their relations with the seismic parameters can be of help in reducing the ambiguity of the inversion solutions and eventually in estimating the petrophysical properties of the specific target reservoir.
This statistical inversion allows the determination of the most likely values of the sought rock properties along with their uncertainty ranges. It results that the porosity is the best resolved rock property, with its most likely value closely approaching the actual value found by the well, even when we insert a somewhat erroneous a-priori information. The hydrocarbon saturation is the second best resolved parameter, but its most likely value does not match the well data. The depth of the target interface is the least resolved parameter and its most likely value is strongly dependent on a-priori information.
Although no general conclusions can be drawn from the results of this exercise, we envisage that the proposed AVA-petrophysical inversion and its possible extensions may be of use for reservoir characterisation
FWI of noisy seismic land data acquired for geothermal exploration
No full textWe present an experience of acoustic FWI on a noisy 2D land dataset to the end of providing a velocity model for direct geological interpretation. We first perform a dedicated processing trying to improve the data quality and we select as the input for the inversion direct, refracted and diving waves. In fact, notwithstanding the processing efforts, reflections are nearly absent. Next, we perform a sequence of FWIs. Since we wish to use the estimated velocity for the interpretation of the area, it is necessary that the estimation is not biased by unverified a-priori geological hypotheses. Therefore, to derive a low-resolution velocity model, we apply two runs of genetic-algorithm FWI (GA-FWI) with different data misfit functions based on envelopes and on waveforms. In fact, GA do not start from a given velocity model, thus risking to bias the final outcome, but from an ensemble of models randomly selected within large search ranges. The GA velocities are then used as starting model for a gradient-based FWI which yields an improved model, appropriate for evaluating different geological hypotheses. In two locations, the check-shot velocities of exploratory wells show a good matching with the 1D velocity profiles extracted from the final model
Comparison of object functions for the inversion of seismic data and study on the potentialities of the Wasserstein Metric
No full textPartial and total ankle allografts
No full textINTRODUCTION: Osteochondral lesions of the ankle joint still represents a challenge for orthopedic surgeons. In recent
years, biological treatment solutions such as osteochondral allograft transplantation have been proposed. The aim of this
review article is to report about current concepts and results of partial and total allograft in the ankle joint.
EVIDENCE ACQUISITION: All studies published in PubMed from 2000 to September 2020 regarding partial and total
ankle allografts in the ankle joint were identified, considering the following criteria: level I-IV evidence addressing the
areas of interest outlined above; measures of functional, clinical, or imaging outcome; and outcome related to ankle cartilage lesions or ankle arthritis treated by allografts.
EVIDENCE SYNTHESIS: The number of selected articles was 21; 11 of those focused on partial allografts and 10 on
bipolar fresh osteochondral allografts. All papers presented are case series.
CONCLUSIONS: Osteochondral allograft in the ankle joint represent a concrete option to repair major osteochondral
defects. Despite the literature showed interesting findings, many controversies remain regarding the use of total ankle
allograft transplantations and their superiority compared to standard technique
Velocity model estimation by means of Full Waveform Inversion of transmitted waves: An example from a seismic profile in the geothermal areas of Southern Tuscany, Italy
No full textWe propose an FWI strategy that makes use of transmitted waves as input data and utilizes both global and local optimization methods to estimate the P-wave velocity model of the subsurface. We envisage that our approach may be applicable to difficult seismic land data, like those from geothermal areas characterised by complex geological structures. As a test case, we considered the CROP/18A seismic reflection profile that crosses the geothermal field of Larderello (southern Tuscany, Italy). The aim is to estimate the P-wave velocity model down to a few kilometres depth below the surface that could be used as complementary information to the standard seismic reflection image which, in this case, does not show interpretable reflections in a range of depths accessible to industrial drillings. One innovative aspect of the inversion we propose with respect to conventional FWI approaches is its independence of a starting model that, ideally, should reproduce the true long wavelength velocity structure of the subsurface and that may be rather difficult to obtain in case of low quality data and complex geology. We lessen the dependence on knowledge of a suitable starting model by performing a sequence of two inversions. First, we employ a genetic-algorithm (GA) based inversion, a global optimisation method that does not require any specific starting model, resulting in a long wavelength, low-resolution velocity model. This model then becomes the starting model for a second FWI, driven by a local optimisation algorithm, aimed at bringing in the fine details of the subsurface velocity structure. The reliability of the final model is checked by comparing observed and predicted waves for many common shot gathers along the seismic line and through the matching between the velocities measured by check shots in two nearby wells and the FWI velocities in the same locations. Many details of the velocity field, likely related to metamorphic and igneous formations, become apparent and may complement the interpretation of the standard reflection image. From these results, it appears that the use of transmitted waves and of the FWI approach discussed here may effectively improve the information for geophysical interpretation of challenging seismic land data, such as those that characterise many areas of geothermal exploration
Surface Wave FWI on Complex Models - The Robustness of the Inversion to Assumptions and Forward Modeling Approximations
No full textFull waveform inversion (FWI) of surface waves with genetic algorithm (GA) is able to invert complex near surface models even in the case where very limited a-priori information is available, but it requires long computing time. One way to reduce the computing time is to make assumptions on the subsurface and to simplify the forward modelling. By using a few complex near surface models, with velocity inversions, lateral velocity variations and with an irregular topographic surface, we discuss how the following issues affect the inversion results in terms of either the data misfit or the model misfit: 1) fixing the compressional wave velocities and densities to the estimated shear wave velocities according to empirical equations, instead of inverting them; 2) neglecting attenuation in the forward modelling; 3) performing 2D forward modelling and applying a 3D to 2D correction to the observed data. Although these approximations degrade model prediction, yet the main features of the shear wave models can be retrieved. Instead, the data prediction is always satisfactory, showing again that theoretical approximations in the forward modelling affect more the model misfit than the data misfit
Two-grid Full Waveform Rayleigh Wave Inversion by Means of Genetic Algorithm with Frequency Marching
No full textWe present a 2D elastic full waveform inversion (FWI) of Rayleigh waves (RW) with a genetic algorithm (GA) as the optimization tool and with a finite difference code as the forward modeling engine. To limit the computing time required by GA, we implement the RW FWI, making use of a two-grid parametrization of the subsurface model, one fine grid and one coarse grid, and of frequency marching during the evolution of the GA optimization. Forward modeling is performed on the fine grid to avoid numerical dispersion, while the GA inverts for the unknown velocities and densities at the nodes of the coarse grid. The coarser the grid the less the unknowns to be inverted for, at the expense of the final model resolution. Frequency marching also speeds up convergence because it has the ability of rejecting unrealistic models at the initial generations of the GA. Due to the very band-limited nature of RW, we suggest to start frequency marching from near the peak frequency of RW. Synthetic examples reproducing velocity inversions, lateral velocity variations and varying elevations show the feasibility of the proposed RW FWI, without any a-priori information and with shear-wave and
compressional-wave velocities and densities as unknowns
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