Impact of spatial correlation of ground motions on seismic damage for residential buildings in Bucharest,Romania

Natural Hazards - This paper focuses on the investigation of seismic risk for residential buildings situated in Bucharest, the capital city of Romania. With a population of nearly 2 million...     

Non-ENSO interannual rainfall variability in central Chile during austral winter

The first principal component (PC1) of seasonal rainfall anomalies in central Chile during winter (June–August) is used to analyze the circulation anomalies related to wet and dry conditions, when near-normal or neutral SST anomalies are observed in the equatorial Pacific, i.e., during non-ENSO conditions. Eight wet and eight dry winter seasons were defined as the upper and lower terciles of PC1 for 24 non-ENSO winters in the period 1958–2000. Unlike the single process attributed to ENSO, during non-ENSO winter seasons, there are several sources triggering or modifying the propagation of the stationary waves that impact the rainfall regime in central Chile. Unfortunately, the multiple processes that seem to be involved in the modulation of the interannual rainfall variability in central Chile, as seen in this work, limit the predictability of rainfall during non-ENSO conditions.     

Parameter identification of minifrac numerical tests using a gradient boosting-based proxy model and genetic algorithm

In recent years, the petroleum industry has devoted considerable attention to studying fluid flow inside fracture channels due to the discovery of naturally fractured reservoirs. The behavior prediction of these reservoirs is a well-known challenging task, in which the initial stage consists of identifying reservoir hydromechanical parameters. This work proposes an artificial intelligence-based approach to identify hydromechanical parameters from borehole injection pressure curves acquired through minifrac tests. This approach combines proxy modeling with a stochastic optimization algorithm to match observed and predicted borehole pressure curves. Therefore, a gradient boosting-based proxy model is built to predict borehole pressure curves, considering a proper strategy to develop time series modeling. Moreover, a Bayesian optimization algorithm is applied to compute the gradient boosting hyperparameters. In this optimization scenario, this paper proposes an appropriate objective function established from the assumed time series prediction strategy and the k-fold cross-validation. Finally, a genetic algorithm is adopted to identify unknown hydromechanical parameters, solving an inverse problem. Based on the proposed workflow, a study of the importance of the hydromechanical parameters is developed. To assess the methodology applicability, the approach is employed to identify parameters in synthetic and field minifrac tests. The results present how this approach can adequately identify hydromechanical parameters of hydraulic fracturing problems.