Faculty Opinions recommendation of A wellness study of 108 individuals using personal, dense, dynamic data clouds.

In recent decades, mechanisms for observation and information production have proliferated in an attempt to meet the growing needs of stakeholders to access dynamic data for the purposes of informed decision-making. In the land sector, a growing number of land observatories are producing data and ensuring its transparency. We hypothesize that these structures are being developed in response to the need for information and knowledge, a need that is being driven by the scale and diversity of land issues. Based on the results of a study conducted on land observatories in Africa, this paper presents existing and past land observatories on the continent and proposes to assess their diversity through an analysis of core dimensions identified in the literature. The analytical framework was implemented through i) an analysis of existing literature on land observatories, ii) detailed assessments of land observatories based on semi-open interviews conducted via video conferencing, iii) fieldwork and visits to several observatories, and iv) participant observation through direct engagement and work at land observatories. We emphasize that the analytical framework presented here can be used as a tool by land observatories to undertake ex-post self-evaluations that take the observatory’s trajectory into account, or in the case of proposed new land observatories, to undertake ex-ante analyses and design the pathway towards the intended observatory.

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Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

Journal of Petroleum Exploration and Production Technology ◽

10.1007/s13202-021-01210-5 ◽

2021 ◽

Author(s):

Seyed Kourosh Mahjour ◽

Antonio Alberto Souza Santos ◽

Manuel Gomes Correia ◽

Denis José Schiozer

Keyword(s):

Flow Simulation ◽

Reduction Process ◽

Computational Time ◽

Metaheuristic Algorithm ◽

Dynamic Data ◽

Scenario Reduction ◽

Field Development ◽

Data Usage ◽

Simulation Process ◽

Uncertainty Space

AbstractThe simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

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