In this work, a mathematical model is developed for production data analysis (PDA) in complex fracture-network horizontal wells, and the PDA is mainly based on the log-log plots of rate-normalized pressure (RNP) versus material-balance time. Stimulated region (Inner Region) and non-stimulated region (Outer Region) with different permeability caused by stimulated reservoir volume (SRV) effects are also considered in the mathematical model. The mathematical model is solved by semi-analytical approach with discretization of fracture networks and region boundaries. With the model solutions, verifications based on a numerical model and sensitivity analysis based on the proposed model are all performed
The comparison results show that the proposed model matches well with the numerical simulator. For the well productivity, it increases with the increases in outer radius and inner radius, while it decreases with the increases in mobility ratio and capacity ratio. Like pressure difference and its derivative, the flow regimes of production behaviors are identified based on RNP and RNP’. According to their special features, it is learnt that the production behaviors of horizontal wells with complex fracture networks can be divided into eight stages. Similar to conventional composite formation, the mobility ratio mainly affects the transitional flow, flow in Outer Region, and boundary-dominated flow. The horizontal value of RNP derivative in Outer Region is 0.5M. The larger the capacity ratio is, the steeper the RNP curves in late stages go up. With the increase in capacity ratio, the boundary-dominated flow appears earlier as the elastic energy of Outer Region is smaller. In addition, with the increase of inner radius, the pseudo radial flow in Inner Region lasts longer, and the appearances of transition flow and flow in Outer Region are later. The starting time of boundary-dominated flow is completely controlled by dimensionless outer radius.
Posed for embedding multi-networks 28, where the multi-network is a more general concept than the multi-view network and allows many-to-many correspondence across networks. Xvision driver download for windows 10. While the proposed model can be applied to the more specific multi-view networks, it does not focus on the study of the objectives of multi-view network embedding. Xinwei Yue (Member, IEEE) received the Ph.D. Degree in communication and information system from Beihang University (BUAA), Beijing, China, in 2018. He has been a Lecturer with the School of Information and Communication Engineering, Beijing Information Science and Technology University (BISTU), Beijing, since 2018 and promoted to an Associate. Active distribution network planning-operation co-optimization considering the coordination of ESS and DG XW Shen, SZ Zhu, JH Zheng, YD Han, QS Li Power Syst Technol 39 (7), 1913-1920, 2015.
Abstract: Heterogeneous information networks (HINs) with rich semantics are ubiquitousin real-world applications. For a given HIN, many reasonable clustering resultswith distinct semantic meaning can simultaneously exist. User-guided clusteringis hence of great practical value for HINs where users provide labels to asmall portion of nodes. To cater to a broad spectrum of user guidance evidencedby different expected clustering results, carefully exploiting the signalsresiding in the data is potentially useful. Meanwhile, as one type of complexnetworks, HINs often encapsulate higher-order interactions that reflect theinterlocked nature among nodes and edges. Network motifs, sometimes referred toas meta-graphs, have been used as tools to capture such higher-orderinteractions and reveal the many different semantics. We therefore approach theproblem of user-guided clustering in HINs with network motifs. In this process,we identify the utility and importance of directly modeling higher-orderinteractions without collapsing them to pairwise interactions. To achieve this,we comprehensively transcribe the higher-order interaction signals to a seriesof tensors via motifs and propose the MoCHIN model based on joint non-negativetensor factorization. This approach applies to arbitrarily many, arbitraryforms of HIN motifs. An inference algorithm with speed-up methods is alsoproposed to tackle the challenge that tensor size grows exponentially as thenumber of nodes in a motif increases. We validate the effectiveness of theproposed method on two real-world datasets and three tasks, and MoCHINoutperforms all baselines in three evaluation tasks under three differentmetrics. Additional experiments demonstrated the utility of motifs and thebenefit of directly modeling higher-order information especially when userguidance is limited.
Submission history
From: Yu Shi [view email][v1] Wed, 28 Nov 2018 00:16:03 UTC (614 KB)
[v2] Thu, 27 Jun 2019 02:51:29 UTC (1,509 KB)[v3]Sun, 22 Sep 2019 22:39:09 UTC (1,048 KB)
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Xinwei He
Naijing Zhang
Carl Yang
Jiawei Han
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