GNGTS 2017 - 36° Convegno Nazionale

GNGTS 2017 S essione 3.1 533 References Aki, K., and Richards, P. G. 1980: Quantative seismology: Theory and methods. New York, 801. Aleardi, M., Ciabarri, F., Peruzzo, F., Garcea, B., and Mazzotti, A. 2016a: Bayesian estimation of reservoir properties by means of wide-angle AVA inversion and a petrophysical Zoeppritz Equation. In 78th EAGE Conference and Exhibition 2016. Doi: 10.3997/2214-4609.201601551. Aleardi, M., Ciabarri, F., Garcea, B., Peruzzo, F., and Mazzotti, A. 2016b: Probabilistic seismic-petrophysical inversion applied for reservoir characterization in offshore Nile delta. In 78th EAGE Conference and Exhibition 2016. Doi: 10.3997/2214-4609.201600969 Avseth, P., Mukerji, T., and Mavko, G. 2005: Quantitative seismic interpretation. Cambridge university press. Bongajum, E. L., Boisvert, J., and Sacchi, M. D. 2013: Bayesian linearized seismic inversion with locally varying spatial anisotropy. Journal of Applied Geophysics, 88, 31-41. Buland, A., Kolbjørnsen, O., and Omre, H. 2003: Rapid spatially coupled AVO inversion in the Fourier domain. Geophysics, 68(3), 824-836. Tarantola, A. 2005: Inverse problem theory and methods for model parameter estimation. Society for Industrial and Applied Mathematics. Tetyukhina, D., van Vliet, L. J., Luthi, S. M., and Wapenaar, K. 2010: High-resolution reservoir characterization by an acoustic impedance inversion of a Tertiary deltaic clinoform system in the North Sea. Geophysics. Zunino, A., Mosegaard, K., Lange, K., Melnikova, Y., and Mejer Hansen, T. 2014: Monte Carlo reservoir analysis combining seismic reflection data and informed priors. Geophysics, 80(1), R31-R41. Target-oriented, structurally constrained seismic-petrophysical inversion M. Aleardi 1 , F. Ciabarri 2 , T. Gukov 2 1 Earth Sciences Department, University of Pisa, Italy 2 Edison Introduction. Seismic-petrophysical inversion is a common technique used to obtain information about the 2D and 3D structure of the reservoir. The main objective is to infer quantitative rock properties (i.e. porosity, shaliness, fluid saturation) from seismic reflection data (Aleardi et al., 2016). There are several issues with inverting seismic reflection data; among them limited data bandwidth, data noise, incomplete data coverage, and imperfect model parameterization. As a result, geophysical inverse problems are often ill-posed, which means that the subsurface properties cannot uniquely recovered. To overcome this problem, additional constraints or regularizations are usually added into the inversion kernel (i.e. a priori information estimated from well-log data). In most cases, seismic-petrophysical inversion inverts each data gather independently, for example assuming a 1D forward model. As a result, the inversion outcomes are sensitive to data noise and when combined to form a 2D or 3D image, the resulting image can be noisy, and important geologic features can be masked. In addition, seismic inversion techniques based on a least-squares approach with Tikhonov regularization, can result in smoothed and unfocused transitions between vertical or lateral formation boundaries. For these reasons, lateral constraints have been extensively used in seismic inversion. Buland et al. (2003) described an approach in which the model parameters are assumed to be second-order stationary, and an isotropic correlation function is used to impose lateral correlation. However, the assumption of second-order stationarity is invalid in a complex reservoir with nonlinear geologic features. Furthermore, the data required to construct a reliable correlation function are often unavailable. Tetyukhina et al. (2010) used a trace by trace inversion with stratigraphic constraints to produce high-resolution characterization of fluvio-deltaic sequences. Bongajum et al. (2013) used locally varying anisotropy to insert nonlinear geologic features into the inversion kernel. Also,