GEOSTATISTICAL SEISMIC INVERSION for FRONTIER EXPLORATION(Article)

Numerical three-dimensional high resolution models of the subsurface petro-elastic properties are key tools for both exploration and production stages. Stochastic seismic inversion techniques are often used to infer the spatial distribution of the properties of interest by integrating simultaneously seismic reflection and well-log data also allowing accessing the spatial uncertainty of the retrieved models. In frontier exploration areas the available dataset is often composed exclusively by seismic reflection data due to the lack of drilled wells and therefore of high uncertainty. In these cases, subsurface models are usually retrieved by deterministic seismic inversion methodologies based exclusively on the existing seismic reflection data and an a priori elastic model. The resulting models are smooth representations of the real complex geology and do not allow assessing the uncertainty. To overcome these limitations we introduce a geostatistical framework that allows inverting seismic reflection data without the need of experimental data (i.e., well-log data) within the inversion area. This iterative geostatistical seismic inversion methodology integrates simultaneously the available seismic reflection data and information from geological analogs (nearby wells and/or analog fields) allowing retrieving acoustic impedance models. The model parameter space is perturbed by stochastic sequential simulation methodology that handles non-stationary probability distribution function. The convergence from iteration to iteration is ensured by a genetic-algorithm driven by the trace-by-trace mismatch between real and synthetic seismic reflection data. The method was successfully applied to a frontier basin in the offshore Southwest Europe where no well has been drilled yet. Geological information about the expected impedance distribution was retrieved from nearby wells and integrated within the inversion procedure. Resulting acoustic impedance models are geological consistent with the available information and data, and the match between the inverted and the real seismic data ranges from 85% to 90% in some regions. © 2017 Society of Exploration Geophysicists and American Association of Petroleum Geologists.