Revitalising critical components of urban decay features

Conservation and sustainability of historic cities in Malaysia have been drawing a lot attention from different stakeholders as many researches were undertaken relating heritage conservation and heritage tourism. The declaration of historic cities in Penang and Malacca as World Heritage Cities by UNESCO had proven that the conservation and preservation of tangible heritage assets not only essential to sustain the continuity of local cultural identity but also contribute to the economic regeneration and domestic heritage tourism. However, there have been only a small number of studies done on the urban decay features experienced by old towns in Malaysia and the corresponding revitalisation tools which suit the local culture context. Based upon the conflicts between urban decay issues arise within Ipoh Old Town and the lack of effective revitalisation efforts, the objectives are to identify the urban decay features currently experienced by Ipoh Old Town and to recommend the critical components to be revitalised in Ipoh Old Town. The mixed methods research which incorporated both quantitative and qualitative research approaches is adopted as the methodology for this study. The quantitative data is obtained through questionnaire surveys whereas the qualitative research methods involved the gathering of information through interviews and case study analysis. These suggestions are categories into three major themes which are revitalisation, stricter enforcement and financial funding

Well Placement Optimization with the Covariance Matrix Adaptation Evolution Strategy and Meta-Models

International audienceThe 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: (1) Adaptive penalization with rejection in order to handle well placement constraints; (2) 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, 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 metamodel leads to further improvement, which was around 20% for the synthetic case in this study