Verdaz (Brazil)

Este artigo parte da pesquisa em Artes Visuais desenvolvida no Mestrado em Artes da UFPEL/RS. Na pesquisa busco pensar através do desenho e de uma relação possível entre os animais e a ideia de classe social, de formas propositivas e lúdicas de experiência em arte. Como estratégia expansiva, me aproprio de elementos da cultura e crio cartas de baralho a fim de instaurar, via espaço do jogo, uma situação dialógica entre ecologia e classes. Para isso busco a história do baralho, e suas formas de representação do campo social. Analiso as séries de desenhos para as cartas do baralho e identifico alguns conceitos trabalhados nas Artes Visuais como Colecionismo, Enciclopedismo e a relação da escrita nas artes na criação de verbetes de cada naipe.

Abstract This machine learning study incorporates geoscience and engineering data to characterize which geological, reservoir and completion data contribute most significantly to well production performance. A better understanding of the key factors that predict well performance is essential in assessing the commercial viability of exploration and development, in the optimization of capital spending to increase rates of return, and in reserve and resource evaluations. Machine learning models provide an objective, analytical means to interpret large, complex datasets. Generally, such models demand large databases of consistently evaluated data. As geological data is interpretive, often varying from one geologist to another, or from one pool to another, it can be difficult to incorporate geological data into regional machine learning models. Consequently, efforts to use machine learning in the oil and gas industry to predict well performance are often focused exclusively on engineering completion technology. However, this case study has utilized a regional geological Spirit River database with consistent petrophysical evaluation methodology across the entire play. This geological database is complemented with public completion and fracture data and production data to build predictive models using inputs from all subsurface disciplines. Redundancies in the data were identified and removed. Features explaining a significant proportion of the variance in production were also removed if their effect was captured by more fundamental, correlated features that were more straightforward to interpret. The dataset was distilled to 13 key features providing predictions with a similar precision to those obtained using the full-featured dataset. The thirteen features in this case study are a combination of geological, reservoir and completion data, underlining that an approach integrating both geoscience and engineering data is vital to predicting and optimizing well performance accurately for future wells.