Shear wave velocities of the crust and upper mantle are defined beneath the Roccamonfina volcano and surrounding Apennines (southern Italy) from the simultaneous nonlinear inversion of the local group velocity dispersion data, obtained from seismic events recorded in 1988-2004 at Roccamonfina station of the INGV-RSNC network, and regional dispersion data obtained in previous studies. The main features of the representative V S models are a carbonatic basement and a low velocity zone at 6-10 km of depth. The sedimentary succession is *5 km thick below the Roccamonfina volcano and lays above a high V S (3.8 km/s) ascribable to solidified magma body, while it is *10 km thick below the surrounding Apennines. A low velocity layer with an average thickness of 10 km is detected below the Roccamonfina volcano which can be associated with the presence of partial melting and interpreted as magmatic reservoir. Such low velocity layer, also found below the surrounding Apennines but with a reduced thickness of 2-3 km, extends to the Campanian Plain and to the Neapolitan volcanic area, from Campi Flegrei to Somma-Vesuvius.

The objective of this work is to describe a comprehensive approach integrating seismic data processing and sets of wireline logs for reservoir characterization of one of the tight gas plays of the Dnieper-Donets basin. This paper intends to discuss a case study from seismic data processing, integrating seismic attributes with formation properties from logs in a geocellular model for sweet spot selection and risk analysis. The workflow during the project included the following steps.Seismic data 3D processing, including 5D interpolation and PSTM migration.Interpretation of limited log data from 4 exploration and appraisal wells.Seismic interpretation and inversion.Building a static model of the field.Recommendations for drilling locations.Evaluation of the drilled well to verify input parameters of the initial model. The static model integrated all available subsurface data and used inverted seismic attributes calibrated to the available logs to constrain the property modelling. Then various deterministic and stochastic approaches were used for facies modeling and estimation of gas-in-place volume. Integrating all the available data provides insights for better understating the reservoir distribution and provided recommendations for drilling locations. Based on the combination of the geocellular model, seismic attributes and seismic inversion results, the operator drilled an exploration well. The modern set of petrophysical logs acquired in the recently drilled well enforced prior knowledge and delivered a robust picture of the tight gas reservoir. The results from the drilled well matched predicted formation properties very closely, which added confidence in the technical approach applied in this study and similar studies that followed later. It is the fork in the road moment for the Dnieper-Donetsk basin with huge tight gas potential in the region that inspires for exploration of other prospects and plays. A synergy of analytical methods with a combination of seismic processing, geomodeling, and reservoir characterization approaches allowed accurate selection of the drilling targets with minimum risk of "dry hole" that has been vindicated by successful drilling outcome in a new exploration well.

The seismic data have historically been utilized to perform structural interpretation of the geological subsurface. Modern approaches of Quantitative Interpretation are intended to extract geologically valuable information from the seismic data. This work demonstrates how rock physics enables optimal prediction of reservoir properties from seismic derived attributes. Using a seismic-driven approach with incorporated prior geological knowledge into a probabilistic subsurface model allowed capturing uncertainty and quantifying the risk for targeting new wells in the unexplored areas. Elastic properties estimated from the acquired seismic data are influenced by the depositional environment, fluid content, and local geological trends. By applying the rock physics model, we were able to predict the elastic properties of a potential lithology away from the well control points in the subsurface whether or not it has been penetrated. Seismic amplitude variation with incident angle (AVO) and azimuth (AVAZ) jointly with rock-derived petrophysical interpretations were used for stochastical modeling to capture the reservoir distribution over the deep Visean formation. The seismic inversion was calibrated by available well log data and by traditional structural interpretation. Seismic elastic inversion results in a deep Lower Carboniferous target in the central part of the DDB are described. The fluid has minimal effect on the density and Vp. Well logs with cross-dipole acoustics are used together with wide-azimuth seismic data, processed with amplitude control. It is determined that seismic anisotropy increases in carbonate deposits. The result covers a set of lithoclasses and related probabilities: clay minerals, tight sandstones, porous sandstones, and carbonates. We analyzed the influence of maximum angles determination for elastic inversion that varied from 32.5 to 38.5 degrees. The greatest influence of the far angles selection is on the density. AI does not change significantly. Probably the 38,5 degrees provides a superior response above the carbonates. It does not seem to damage the overall AVA behavior, which result in a good density outcome, as higher angles of incidence are included. It gives a better tie to the wells for the high density layers over the interval of interest. Sand probability cube must always considered in the interpretation of the lithological classification that in many cases may be misleading (i.e. when sand and shale probabilities are very close to each other, because of small changes in elastic parameters). The authors provide an integrated holistic approach for quantitative interpretation, subsurface modeling, uncertainty evaluation, and characterization of reservoir distribution using pre-existing well logs and recently acquired seismic data. This paper underpins the previous efforts and encourages the work yet to be fulfilled on this subject. We will describe how quantitative interpretation was used for describing the reservoir, highlight values and uncertainties, and point a way forward for further improvement of the process for effective subsurface modeling.