987 research outputs found

    PORE-NETWORK MODELING AND ANALYSIS OF LABORATORY INDUCED HYDRAULIC FRACTURES

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    Well stimulation is undertaken to reduce the restriction to flow in a reservoir. Among all the well stimulation techniques, hydraulic fracturing is one of the most widely employed techniques due to the development of shale and tight sand resources. The present study focuses on two problems relevant to hydraulic fracturing; predicting the transport properties enhancement as a function of recorded acoustic emission (AE) events during hydraulic fracturing and predicting the breakdown pressures in cyclic fracturing. To study the first problem, I initiate pore-scale modeling of acoustic emission (AE) events based on percolation theory. The primary objective is to predict the permeability enhancement by accounting for the number of AE events. I first develop a physically representative model of the intact pore space of the matrix of Tennessee sandstone at the core scale based on petrophysical measurements, which are porosity, permeability, and capillary pressure. A block-scale sample of the formation is then hydraulically fractured, where piezoelectric sensors record the events generated during stimulation. I predict the permeability enhancement of the formation at the core scale by accounting for the number of AE events per unit volume. Independent petrophysical measurements corroborate the predicted results based on percolation theory. The proposed model has significant implications for characterizing the transport properties of the stimulated reservoir volume. The second problem is relevant to predicting the breakdown pressure in hydraulic fracturing. In conventional fracturing, the fluid pressure is increased monotonically to reach failure in a single cycle. The breakdown pressure can be reduced if we increase and decrease the fluid pressure cyclically (cyclic fracturing). This phenomenon has been tested in other fields, but it is not yet possible to predict the breakdown pressure and cycle in petroleum engineering in the context of hydraulic fracturing. The present study proposes a workflow based on a modified Paris law to predict the breakdown pressure and cycle of cyclic fracturing. The modified Paris law is based on linear elastic fracture mechanics (LEFM), which treats the solid domain as an isotropic and linear elastic medium. I use the data available in the literature for dry Tennessee sandstones. The samples were hydraulically fractured under triaxial stress, two with conventional and two with cyclic methods. The results show that the tuned Paris law can predict the breakdown pressure and cycle with reasonable accuracy. The tuned model can help us to design an optimum scenario that is fundamentally different from the conventional method for formation stimulation

    "When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams

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    The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.Comment: Accepted as a full paper in the 2nd International Workshop on Social Computing co-located with ICDM, 2018 Singapor

    Parameterized Complexity of Perfectly Matched Sets

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    For an undirected graph G, a pair of vertex disjoint subsets (A, B) is a pair of perfectly matched sets if each vertex in A (resp. B) has exactly one neighbor in B (resp. A). In the above, the size of the pair is |A| (= |B|). Given a graph G and a positive integer k, the Perfectly Matched Sets problem asks whether there exists a pair of perfectly matched sets of size at least k in G. This problem is known to be NP-hard on planar graphs and W[1]-hard on general graphs, when parameterized by k. However, little is known about the parameterized complexity of the problem in restricted graph classes. In this work, we study the problem parameterized by k, and design FPT algorithms for: i) apex-minor-free graphs running in time 2^O(?k)? n^O(1), and ii) K_{b,b}-free graphs. We obtain a linear kernel for planar graphs and k^?(d)-sized kernel for d-degenerate graphs. It is known that the problem is W[1]-hard on chordal graphs, in fact on split graphs, parameterized by k. We complement this hardness result by designing a polynomial-time algorithm for interval graphs
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