Nouvelles données sur le téphra de Sarliève et le téphra CF7, marqueurs chrono-stratigraphiques de Grande Limagne (Massif central,France)

A trachytic tephra, discovered in the ancient lake of Sarliève, ‘Grande Limagne’, has been dated using the thermoluminescence technique. The obtained age, 16±4 ka ($\text{2}\text{σ}$), is older than that of the trachytic volcanoes of the Cha??ne des Puys, the ashes of which have already been locally recognised in the region. Its analysis confirms its originality. In the course of the comparisons made to search for its spring, it appears that the wide-dispersion tephra CF7, beforehand correlated by hypothesis to the Puy de Clierzou, probably originates from the Kilian crater or the Puy de Vasset. To cite this article: D. Miallier et al., C. R. Geoscience 336 (2004).     

Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models

The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non-regularity of the objective function. In this paper, we propose an optimization methodology for determining optimal well locations and trajectories based on the covariance matrix adaptation evolution strategy (CMA-ES) which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. In addition, to improve the optimization procedure, two new techniques are proposed: (a) adaptive penalization with rejection in order to handle well placement constraints and (b) incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function. The approach is applied to the PUNQ-S3 case and compared with a genetic algorithm (GA) incorporating the Genocop III technique for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, and standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic algorithm: It leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations needed to reach a good well configuration. Moreover, coupling CMA-ES with a meta-model leads to further improvement, which was around 20% for the synthetic case in this study.