2019
DOI: 10.1016/j.petrol.2019.04.025
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A new and fast waterflooding optimization workflow based on INSIM-derived injection efficiency with a field application

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Cited by 39 publications
(19 citation statements)
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“…This type SPP in fuzzy scenarios is known as fuzzy SPP (FSPP). There exists many researches in fuzzy shortest pathproblem (FSPP) [2,3,7,9,16,18,19,[21][22][23][24][25][26]28,30,32,35,37,39].…”
Section: Introductionmentioning
confidence: 99%
“…This type SPP in fuzzy scenarios is known as fuzzy SPP (FSPP). There exists many researches in fuzzy shortest pathproblem (FSPP) [2,3,7,9,16,18,19,[21][22][23][24][25][26]28,30,32,35,37,39].…”
Section: Introductionmentioning
confidence: 99%
“…The contribution of stRDF is regulating the representation principle of spatiotemporal data in RDF and standardizing the spatiotemporal data querying. It can also be applied to several spatiotemporal-related applications [12][13][14]. However, the spatial information fails to associate with temporal information in the stRDF model, which means that it has a weak ability to record dynamically changing data.…”
Section: Introductionmentioning
confidence: 99%
“…Clustering uses an unsupervised way to uncover the hidden rules and patterns of human society; it is an indispensable mean to mine the complex real-world data [1]. Over the past few decades, a large number of excellent clustering algorithms have been proposed and expanded, and have demonstrated their power in various fields, such as transportation, meteorology, biology, and so on [2]. However, with the advent of the era of big data, complex data in various applications have hundreds of thousands of dimensions, and are characterized by high noise, irregularity and imbalance [3].…”
Section: Introductionmentioning
confidence: 99%
“…Faced with these new features, traditional clustering algorithms perform poorly and are unsatisfactory. The main reasons are as follows: (1) complex data are in a high-dimensional space and are difficult to process; (2) there are a lot of redundancy and noise attributes in high-dimensional data; (3) the distribution of data is uneven, and the datasets present various irregular shapes; and (4) a lot of outliers are hidden in highdimensional data.…”
Section: Introductionmentioning
confidence: 99%