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Impact of Integrating Initial Guess Models into Geostatistical Seismic InversionNormal access

Authors: P. Pereira, L. Azevedo, R. Nunes and A. Soares
Event name: 78th EAGE Conference and Exhibition 2016
Session: Seismic Reservoir Characterization I - Seismic Inversion Advance
Publication date: 31 May 2016
DOI: 10.3997/2214-4609.201600968
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 947.33Kb )
Price: € 20

Seismic inversion methodologies are key tools in reservoir modeling and characterization for inferring the Earth’s subsurface petro-elastic properties of interest. While deterministic seismic inversion methodologies allow retrieving a single best-fit inverse model with low computational effort, geostatistical inversion procedures are computationally expensive but allow assessing the uncertainty related with the inverse solution. In this work we propose a new approach to accelerate and increase the convergence rate of traditional iterative geostatistical seismic inversion methodologies by integrating an initial guess model (e.g. low-frequency model, a best-fit deterministic solution) as part of the objective function used during the iterative procedure. We show the application of the proposed methodology in both synthetic and real case applications, illustrating its advantages in order to decrease the inversion time while simultaneously increasing the convergence of the iterative geostatistical seismic inversion methodology, as revealed by the global correlation coefficient between real and synthetic seismic data. The application of using geostatistical seismic inversion methodologies by integrating an initial guess model can be considered as the link between deterministic and geostatistical seismic inversion methodologies, taking advantage both inversion solutions in order to retrieve a more realistic approximation of the subsurface geology, decreasing uncertainty and consequently the drilling risks.

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