A new and fast waterflooding optimization workflow based on INSIM-derived injection efficiency with a field application

Zhao, Hui; Xu, Lingfei; Guo, Zhenchao; Liu, Wei; Zhang, Qi; Ning, Xuewei; Li, Guohao; Shi, Lihua

doi:10.1016/j.petrol.2019.04.025

Cited by 39 publications

(19 citation statements)

References 32 publications

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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%

A genetic algorithm for the fuzzy shortest path problem in a fuzzy network

Lü

2020

Complex Intell. Syst.

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The shortest path problem (SPP) is an optimization problem of determining a path between specified source vertex s and destination vertex t in a fuzzy network. Fuzzy logic can handle the uncertainties, associated with the information of any real life problem, where conventional mathematical models may fail to reveal proper result. In classical SPP, real numbers are used to represent the arc length of the network. However, the uncertainties related with the linguistic description of arc length in SPP are not properly represented by real number. We need to address two main matters in SPP with fuzzy arc lengths. The first matter is how to calculate the path length using fuzzy addition operation and the second matter is how to compare the two different path lengths denoted by fuzzy parameter. We use the graded mean integration technique of triangular fuzzy numbers to solve this two problems. A common heuristic algorithm to solve the SPP is the genetic algorithm. In this manuscript, we have introduced an algorithmic method based on genetic algorithm for determining the shortest path between a source vertex s and destination vertex t in a fuzzy graph with fuzzy arc lengths in SPP. A new crossover and mutation is introduced to solve this SPP. We also describe the QoS routing problem in a wireless ad hoc network.

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Section: Introductionmentioning

confidence: 99%

A genetic algorithm for the fuzzy shortest path problem in a fuzzy network

Lü

2020

Complex Intell. Syst.

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“…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%

Algebraic Operations on Spatiotemporal Data Based on RDF

Zhu¹,

Li²,

Bai³

2020

IJGI

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In the context of the Semantic Web, the Resource Description Framework (RDF), a language proposed by W3C, has been used for conceptual description, data modeling, and data querying. The algebraic approach has been proven to be an effective way to process queries, and algebraic operations in RDF have been investigated extensively. However, the study of spatiotemporal RDF algebra has just started and still needs further attention. This paper aims to explore an algebraic operational framework to represent the content of spatiotemporal data and support RDF graphs. To accomplish our study, we defined a spatiotemporal data model based on RDF. On this basis, the spatiotemporal semantics and the spatiotemporal algebraic operations were investigated. We defined five types of graph algebras, and, in particular, the filter operation can filter the spatiotemporal graphs using a graph pattern. Besides this, we put forward a spatiotemporal RDF syntax specification to help users browse, query, and reason with spatiotemporal RDF graphs. The syntax specification illustrates the filter rules, which contribute to capturing the spatiotemporal RDF semantics and provide a number of advanced functions for building data queries.

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“…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%

Enhanced synchronization-inspired clustering for high-dimensional data

Chen

Guo

Liu

et al. 2020

Complex Intell. Syst.

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The synchronization-inspired clustering algorithm (Sync) is a novel and outstanding clustering algorithm, which can accurately cluster datasets with any shape, density and distribution. However, the high-dimensional dataset with high dimensionality, high noise, and high redundancy brings some new challenges for the synchronization-inspired clustering algorithm, resulting in a significant increase in clustering time and a decrease in clustering accuracy. To address these challenges, an enhanced synchronization-inspired clustering algorithm, namely SyncHigh, is developed in this paper to quickly and accurately cluster the high-dimensional datasets. First, a PCA-based (Principal Component Analysis) dimension purification strategy is designed to find the principal components in all attributes. Second, a density-based data merge strategy is constructed to reduce the number of objects participating in the synchronization-inspired clustering algorithm, thereby speeding up clustering time. Third, the Kuramoto Model is enhanced to deal with mass differences between objects caused by the density-based data merge strategy. Finally, extensive experimental results on synthetic and real-world datasets show the effectiveness and efficiency of our SyncHigh algorithm.

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A new and fast waterflooding optimization workflow based on INSIM-derived injection efficiency with a field application

Cited by 39 publications

References 32 publications

A genetic algorithm for the fuzzy shortest path problem in a fuzzy network

A genetic algorithm for the fuzzy shortest path problem in a fuzzy network

Algebraic Operations on Spatiotemporal Data Based on RDF

Enhanced synchronization-inspired clustering for high-dimensional data

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