Iterative geostatistical seismic inversion incorporating local anisotropies
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ORIGINAL PAPER
Iterative geostatistical seismic inversion incorporating local anisotropies Pedro Pereira 1 & Inês Calçôa 1 & Leonardo Azevedo 1 & Rúben Nunes 1 & Amílcar Soares 1 Received: 1 August 2019 / Accepted: 8 April 2020 # Springer Nature Switzerland AG 2020
Abstract Geostatistical seismic inversion methods use stochastic sequential simulation as the model generation and perturbation technique. These stochastic simulation methods use a global variogram model to express the expected spatial continuity pattern of the subsurface elastic properties of interest. The conditioning to a single variogram model is not suitable for complex and nonstationary geological environments, resulting in poor inverted models unable to reproduce non-stationary features such as channels, folds, and faults. The proposed method uses a stochastic sequential simulation and co-simulation method able to cope with spatially varying information using local and independent variogram models. The information about the dip, azimuth, and ranges of the local variogram model is inferred directly from the observed data. First, local dip and azimuth structural volumes are computed from seismic attribute analysis. Then, local variogram models are fitted along the directions estimated from the previous step. This information is used as steering data during the inversion, acting as proxy of the true subsurface geological complexities. Application examples in synthetic and real datasets with complex geometries show the impact of using local anisotropy models in both the reproduction of the original seismic data and the reliability of the inverted models. The resulting inverted models show enhanced consistency, where small-to-large scale discontinuities and complex geometries are better reproduced, allowing reducing the spatial uncertainty associated with the subsurface properties. This work represents a step forward in integrating geological consistency into geostatistical seismic inversion, surpassing the limitation of using a single variogram model to reproduce complex geological patterns. Keywords Local varying anisotropy . Geostatistical seismic inversion . Structural seismic attributes . Local variogram models
1 Introduction Subsurface numerical models are key decision tools that describe the spatial distribution of petro-elastic properties
* Pedro Pereira [email protected] Inês Calçôa [email protected] Leonardo Azevedo [email protected] Rúben Nunes [email protected] Amílcar Soares [email protected] 1
CERENA/DECivil, Arquitetura e Georrecursos, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
through the integration of multiple sources of data related to the subsurface geology. These models are commonly a result of the geo-modelling workflow, which includes a seismic inversion step [3, 23]. In seismic inversion, the seismic data is used as observed measurements to predict the spatial distribution of the subsurface elastic and/or rock properties. Solving the seismic inv
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